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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<title>Self-Improvements in Modern Agentic Systems — Survey Hub</title>
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<div class="brand">SI-Agents Survey</div>
<nav class="nav">
<a href="https://arxiv.org/abs/2607.13104" rel="noopener" target="_blank">Paper</a>
<a href="#abstract">Abstract</a>
<a href="#taxonomy">Taxonomy</a>
<a href="#library">Paper Library</a>
<a href="#community">Community</a>
<a href="#citation">Citation</a>
</nav>
</div>
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<div class="page">
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<div class="side-card">
<div class="side-title">Survey statistics</div>
<div style="font-size:14px;color:#374151;line-height:1.7">
A curated map of self-improvement mechanisms in foundation model-based agentic systems.
</div>
<div class="stat-grid">
<div class="stat"><strong>239</strong><span>Curated papers</span></div><div class="stat"><strong>73</strong><span>FM improvement</span></div><div class="stat"><strong>166</strong><span>Scaffolding improvement</span></div><div class="stat"><strong>15</strong><span>Fine-grained sections</span></div>
</div>
</div>
<div class="side-card toc">
<div class="side-title">Contents</div>
<a href="#abstract">Abstract</a><a href="#overview">Overview</a><a href="#recursive-note">Epigraph</a><a href="#taxonomy">Taxonomy</a><a href="#quickstart">Quick start: representative papers</a><a href="#library">Curated paper library</a>
<a class="sub" href="#sgd">1.1 Intrinsic generative demonstrations</a><a class="sub" href="#evaluative-feedback">1.2 Intrinsic evaluative feedback</a><a class="sub" href="#experience-env">1.3.1 Grounded executable environments</a><a class="sub" href="#experience-world">1.3.2 Generative world models</a><a class="sub" href="#prompt-scalar">2.1.1 Scalar-feedback optimization</a><a class="sub" href="#prompt-qualitative">2.1.2 Qualitative-feedback refinement</a><a class="sub" href="#prompt-evolution">2.1.3 Population-based evolution</a><a class="sub" href="#prompt-textgrad">2.1.4 Textual gradient optimization</a><a class="sub" href="#memory-object">2.2.1 Memory object</a><a class="sub" href="#memory-structure">2.2.2 Memory structure</a><a class="sub" href="#memory-processing">2.2.3 Memory processing</a><a class="sub" href="#tool-routing">2.3.1 Dynamic tool routing</a><a class="sub" href="#tool-refine">2.3.2 Iterative tool refinement</a><a class="sub" href="#tool-create">2.3.3 Autonomous tool creation</a><a class="sub" href="#full-scaffolding">2.4 Full scaffolding</a>
<a href="#reading-guide">How to read this survey</a><a href="#community">Community & feedback</a><a href="#authors">Authors</a><a href="#citation">Citation</a>
</div>
<div class="side-card">
<div class="side-title">Project links</div>
<div class="resource-links">
<a class="btn" href="https://arxiv.org/abs/2607.13104" rel="noopener" target="_blank">arXiv Paper</a>
<a class="btn" href="https://github.com/selfimproving-agent/Awesome-Self-Improving-Agents" rel="noopener" target="_blank">GitHub List</a>
</div>
</div>
<div class="side-card" id="recursive-note">
<div class="side-title">Epigraph</div>
<div class="epigraph">
<div class="epigraph-text">“The first ultraintelligent machine is the last invention that man need ever make.”</div>
<div class="epigraph-author">— I. J. Good (1966)</div>
</div>
</div>
</aside>
<main class="main">
<div class="hero">
<div class="eyebrow">2026 survey webpage</div>
<h1>Self-Improvements in Modern Agentic Systems</h1>
<p class="subtitle">
A survey of how foundation model-based agents improve themselves through model updates,
intrinsic demonstrations, intrinsic evaluative feedback, extrinsic experience, prompt evolution, memory, tool use, and recursive scaffolding.
</p>
<div class="actions">
<a class="btn primary" href="#library">Browse curated papers</a>
<a class="btn" href="#taxonomy">View taxonomy</a>
<a class="btn" href="https://github.com/selfimproving-agent/Awesome-Self-Improving-Agents" rel="noopener" target="_blank">GitHub repository</a>
<a class="btn" href="#community">Community & feedback</a>
</div>
</div>
<section id="abstract">
<h2>Abstract</h2>
<p class="lead">
Foundation model-based agents are moving from static prompt-following systems toward
systems that can improve themselves over time. This survey organizes that transition into
two major branches: <strong>foundation model improvement</strong>, which updates the model itself,
and <strong>scaffolding improvement</strong>, which updates surrounding components such as prompts,
memory, tools, and executable agent logic.
</p>
<p>
Across the survey, the central distinction is between improving the underlying foundation model and
improving the scaffolding around it. This distinction provides a practical way to compare methods,
interpret learning signals, and connect representative papers to broader research trends.
</p>
<div class="note">
The paper list is organized so that readers can move directly from the taxonomy to representative works and
then to broader literature within each mechanism.
</div>
</section>
<section id="overview">
<h2>Overview</h2>
<div class="overview-grid">
<div class="figure-frame">
<button aria-label="Open overview figure in full size" class="image-zoom-trigger" onclick="openImageModal()" type="button">
<img alt="Overview of self-improvement mechanisms in foundation model-based agentic systems" class="overview-image" onerror="this.onerror=null;this.src='assets/fig-si-main-001.png';" src="static/images/fig-si-main-001.png"/>
</button>
<div class="figure-caption">Overview of self-improvement mechanisms in foundation model-based agentic systems.</div>
<div class="zoom-hint">Click the figure to view full screen.</div>
</div>
<div class="overview-note">
<p><strong>Core distinction.</strong> Self-improvement can be framed by asking <em>what is being updated</em>.</p>
<ul>
<li><strong>Foundation model improvement</strong> changes the model itself and tends to be slower, more persistent, and more training-centric.</li>
<li><strong>Scaffolding improvement</strong> changes the operational shell around the model and tends to be faster, cheaper, and more reversible.</li>
<li>The paper library below follows this logic so that taxonomy and literature map onto each other directly.</li>
</ul>
</div>
</div>
</section>
<section id="taxonomy">
<h2>Taxonomy</h2>
<p>
The survey can be read as a structured map of how an FM-based agent improves itself. Each item below links
directly into the corresponding literature section.
</p>
<div class="tax-grid">
<div class="tax-card fm">
<h4>1. Foundation Model Improvement <span class="mini-count">73 papers</span></h4>
<ul>
<li><a href="#sgd">1.1 Intrinsic Generative Demonstrations</a> <span class="mini-count">20</span></li>
<li><a href="#evaluative-feedback">1.2 Intrinsic Evaluative Feedback</a> <span class="mini-count">21</span></li>
<li><a href="#experience-env">1.3.1 Grounded Executable Environments</a> <span class="mini-count">17</span></li>
<li><a href="#experience-world">1.3.2 Generative World Models</a> <span class="mini-count">15</span></li>
</ul>
</div>
<div class="tax-card scaf">
<h4>2. Scaffolding Improvement <span class="mini-count">166 papers</span></h4>
<ul>
<li><a href="#prompt-scalar">2.1 Prompt Optimization</a> <span class="mini-count">37</span></li>
<li><a href="#memory-object">2.2 Memory</a> <span class="mini-count">61</span></li>
<li><a href="#tool-routing">2.3 Tool</a> <span class="mini-count">50</span></li>
<li><a href="#full-scaffolding">2.4 Full Scaffolding</a> <span class="mini-count">18</span></li>
</ul>
</div>
</div>
<div class="note">
Counts indicate the number of curated entries currently listed under each branch and subsection.
</div>
</section>
<section id="quickstart">
<h2>Quick start: representative papers</h2>
<p>
These are not the only important papers in the survey. They are included here as a first reading path for readers
who want to understand the landscape quickly before diving into the full library.
</p>
<div class="card-grid">
<div class="ref-card"><strong><a href="https://arxiv.org/abs/2212.10560" rel="noopener" target="_blank">Self-Instruct</a></strong><div class="meta">2023 · 1.1 Intrinsic Generative Demonstrations</div><p>A canonical example of self-synthetic instruction generation for model alignment.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2212.08073" rel="noopener" target="_blank">Constitutional AI</a></strong><div class="meta">2022 · 1.2 Intrinsic Evaluative Feedback</div><p>An influential AI-feedback framework where model-based judgments are used for alignment and policy optimization.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2411.02337" rel="noopener" target="_blank">WebRL</a></strong><div class="meta">2025 · 1.3.1 Grounded Executable Environments</div><p>A representative web-agent training setup built around reinforcement learning and evolving curricula.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2410.13232" rel="noopener" target="_blank">Web Agents with World Models</a></strong><div class="meta">2025 · 1.3.2 Generative World Models</div><p>A concrete example of using learned environment dynamics to improve web agents.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2303.17651" rel="noopener" target="_blank">Self-Refine</a></strong><div class="meta">2023 · 2.1.2 Qualitative-Feedback Refinement</div><p>A classic self-feedback loop for iterative improvement at the prompt/output level.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2406.07496" rel="noopener" target="_blank">TextGrad</a></strong><div class="meta">2025 · 2.1.4 Textual Gradient Optimization</div><p>An influential formulation of automatic optimization via textual gradients.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2305.10250" rel="noopener" target="_blank">MemoryBank</a></strong><div class="meta">2024 · 2.2.2 Memory Structure</div><p>A representative long-term memory architecture for LLM-based agents.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2305.16291" rel="noopener" target="_blank">Voyager</a></strong><div class="meta">2023 · 2.3.1 Dynamic Tool Routing</div><p>An open-ended embodied agent that accumulates skills and uses tools in a growing scaffold.</p></div><div class="ref-card"><strong><a href="https://arxiv.org/abs/2505.22954" rel="noopener" target="_blank">Darwin Godel Machine</a></strong><div class="meta">2025 · 2.4 Full Scaffolding</div><p>A strong representative for recursive self-improving agents that modify their broader operating logic.</p></div>
</div>
</section>
<section id="library">
<h2>Curated paper library</h2>
<p>
The literature below is organized to match the survey taxonomy. Within each subsection, papers are presented
in roughly chronological order so that readers can follow the development of each research direction.
</p>
<div class="toolbar">
<div class="search-wrap">
<input aria-label="Search papers" id="paperSearch" placeholder="Search by title, venue, or year…" type="search"/>
</div>
<div class="chip-group">
<button class="chip active" data-filter="all" type="button">All</button>
<button class="chip" data-filter="fm" type="button">Foundation model</button>
<button class="chip" data-filter="scaffolding" type="button">Scaffolding</button>
<button class="chip" data-filter="recent" type="button">2025+</button>
<button class="chip" data-filter="code" type="button">Has code</button>
</div>
<div class="toolbar-meta"><span id="visibleCount">239</span> visible papers</div>
</div>
<div class="library-block" id="sgd">
<div class="block-head">
<div>
<h3>1.1 Intrinsic Generative Demonstrations</h3>
<p>The agent or model improves by synthesizing demonstrations, instruction sets, reasoning traces, or task distributions that can be used for imitation-style parameter updates.</p>
</div>
<div class="count-badge">20 papers</div>
</div>
<div class="table-wrap">
<table class="paper-table">
<thead>
<tr><th>Year</th><th>Title</th><th>Venue</th><th>Links</th></tr>
</thead>
<tbody>
<tr class="paper-row" data-branch="fm" data-search="2023 self-instruct: aligning language models with self-generated instructions acl" data-year="2023">
<td class="year">2023</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2212.10560" rel="noopener" target="_blank">Self-Instruct: Aligning Language Models with Self-Generated Instructions</a>
</td>
<td>ACL</td>
<td class="links"><a href="https://arxiv.org/abs/2212.10560" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/yizhongw/self-instruct" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2023 large language models can self-improve emnlp" data-year="2023">
<td class="year">2023</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2210.11610" rel="noopener" target="_blank">Large Language Models Can Self-Improve</a>
</td>
<td>EMNLP</td>
<td class="links"><a href="https://arxiv.org/abs/2210.11610" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2023 orca: progressive learning from complex explanation traces of gpt-4 arxiv" data-year="2023">
<td class="year">2023</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2306.02707" rel="noopener" target="_blank">Orca: Progressive Learning from Complex Explanation Traces of GPT-4</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2306.02707" rel="noopener" target="_blank">Paper</a> · <a href="https://aka.ms/orca-lm" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2024 self: self-evolution with language feedback arxiv" data-year="2024">
<td class="year">2024</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2310.00533" rel="noopener" target="_blank">SELF: Self-Evolution with Language Feedback</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2310.00533" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2024 self-guide: better task-specific instruction following via self-synthetic finetuning colm" data-year="2024">
<td class="year">2024</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2407.12874" rel="noopener" target="_blank">SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning</a>
</td>
<td>COLM</td>
<td class="links"><a href="https://arxiv.org/abs/2407.12874" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/zhaochenyang20/Prompt2Model-Self-Guide" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 improving model alignment through collective intelligence of open-source llms icml" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.03059" rel="noopener" target="_blank">Improving Model Alignment Through Collective Intelligence of Open-Source LLMS</a>
</td>
<td>ICML</td>
<td class="links"><a href="https://arxiv.org/abs/2505.03059" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 superficial self-improved reasoners benefit from model merging emnlp" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2503.02103" rel="noopener" target="_blank">Superficial Self-Improved Reasoners Benefit from Model Merging</a>
</td>
<td>EMNLP</td>
<td class="links"><a href="https://arxiv.org/abs/2503.02103" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/xiangchi-yuan/merge_syn" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 will pre-training ever end? a first step toward next-generation foundation mllms via self-improving systematic cognition arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2503.12303" rel="noopener" target="_blank">Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2503.12303" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/thunlp/SICOG?tab=readme-ov-file" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 taskcraft: automated generation of agentic tasks arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2506.10055" rel="noopener" target="_blank">TaskCraft: Automated Generation of Agentic Tasks</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2506.10055" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/OPPO-PersonalAI/TaskCraft" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 iterative tool usage exploration for multimodal agents via step-wise preference tuning neurips" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2504.21561" rel="noopener" target="_blank">Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuning</a>
</td>
<td>NeurIPS</td>
<td class="links"><a href="https://arxiv.org/abs/2504.21561" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/SPORT-Agents/SPORT-Agents" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 maximizing confidence alone improves reasoning arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.22660" rel="noopener" target="_blank">Maximizing Confidence Alone Improves Reasoning</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.22660" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/satrams/rent-rl" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 dive: diversified iterative self-improvement arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2501.00747" rel="noopener" target="_blank">DIVE: Diversified Iterative Self-Improvement</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2501.00747" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/qinyiwei/DIVE" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 self-adapting language models neurips" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2506.10943" rel="noopener" target="_blank">Self-Adapting Language Models</a>
</td>
<td>NeurIPS</td>
<td class="links"><a href="https://arxiv.org/abs/2506.10943" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/Continual-Intelligence/SEAL" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 first sft, second rl, third upt: continual improving multi-modal llm reasoning via unsupervised post-training neurips" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/pdf/2505.22453" rel="noopener" target="_blank">First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training</a>
</td>
<td>NeurIPS</td>
<td class="links"><a href="https://arxiv.org/pdf/2505.22453" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/waltonfuture/MM-UPT" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 ladder: self-improving llms through recursive problem decomposition arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2503.00735" rel="noopener" target="_blank">LADDER: Self-Improving LLMs Through Recursive Problem Decomposition</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2503.00735" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 self-consistency preference optimization icml" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2411.04109" rel="noopener" target="_blank">Self-Consistency Preference Optimization</a>
</td>
<td>ICML</td>
<td class="links"><a href="https://arxiv.org/abs/2411.04109" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 reinforcing general reasoning without verifiers iclr" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.21493" rel="noopener" target="_blank">Reinforcing General Reasoning Without Verifiers</a>
</td>
<td>ICLR</td>
<td class="links"><a href="https://arxiv.org/abs/2505.21493" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/sail-sg/VeriFree" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 sage: multi-agent self-evolution for llm reasoning arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2603.15255" rel="noopener" target="_blank">SAGE: Multi-Agent Self-Evolution for LLM Reasoning</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2603.15255" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 andes: agent native data evolving synthesis tool for autonomous instruction alignment arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2606.01279" rel="noopener" target="_blank">ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2606.01279" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/zzy1127/ANDES" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 evoground: self-evolving video agents for video temporal grounding arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2605.13803" rel="noopener" target="_blank">EvoGround: Self-Evolving Video Agents for Video Temporal Grounding</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2605.13803" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/minjoong507/EvoGround" rel="noopener" target="_blank">Code</a></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="library-block" id="evaluative-feedback">
<div class="block-head">
<div>
<h3>1.2 Intrinsic Evaluative Feedback</h3>
<p>The system derives its own reward, critique, verification signal, or intrinsic supervision to guide further updates.</p>
</div>
<div class="count-badge">21 papers</div>
</div>
<div class="table-wrap">
<table class="paper-table">
<thead>
<tr><th>Year</th><th>Title</th><th>Venue</th><th>Links</th></tr>
</thead>
<tbody>
<tr class="paper-row" data-branch="fm" data-search="2025 strive: structured reasoning for self-improvement in claim verification mir" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2502.11959" rel="noopener" target="_blank">STRIVE: Structured Reasoning for Self-Improvement in Claim Verification</a>
</td>
<td>MIR</td>
<td class="links"><a href="https://arxiv.org/abs/2502.11959" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 beyond accuracy: the role of calibration in self-improving large language models arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2504.02902" rel="noopener" target="_blank">Beyond Accuracy: The Role of Calibration in Self-Improving Large Language Models</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2504.02902" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2022 constitutional ai: harmlessness from ai feedback arxiv" data-year="2022">
<td class="year">2022</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2212.08073" rel="noopener" target="_blank">Constitutional AI: Harmlessness from AI Feedback</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2212.08073" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/anthropics/ConstitutionalHarmlessnessPaper?tab=readme-ov-file" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2023 rest meets react: self-improvement for multi-step reasoning llm agent arxiv" data-year="2023">
<td class="year">2023</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2312.10003" rel="noopener" target="_blank">ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2312.10003" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 self-evolved reward learning for llms iclr" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2411.00418" rel="noopener" target="_blank">Self-Evolved Reward Learning for LLMs</a>
</td>
<td>ICLR</td>
<td class="links"><a href="https://arxiv.org/abs/2411.00418" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/microsoft/DKI_LLM/tree/main/SER" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 sample, predict, then proceed: self-verification sampling for tool use of llms arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2506.02918v1" rel="noopener" target="_blank">Sample, Predict, then Proceed: Self-Verification Sampling for Tool Use of LLMs</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2506.02918v1" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 rlsr: reinforcement learning from self reward arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.08827" rel="noopener" target="_blank">RLSR: Reinforcement Learning from Self Reward</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.08827" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 right question is already half the answer: fully unsupervised llm reasoning incentivization neurips" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2504.05812" rel="noopener" target="_blank">Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization</a>
</td>
<td>NeurIPS</td>
<td class="links"><a href="https://arxiv.org/abs/2504.05812" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/QingyangZhang/EMPO" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 ttrl: test-time reinforcement learning neurips" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2504.16084" rel="noopener" target="_blank">TTRL: Test-Time Reinforcement Learning</a>
</td>
<td>NeurIPS</td>
<td class="links"><a href="https://arxiv.org/abs/2504.16084" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/PRIME-RL/TTRL" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 can large reasoning models self-train? arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.21444" rel="noopener" target="_blank">Can Large Reasoning Models Self-Train?</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.21444" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/tajwarfahim/srt" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 self rewarding self improving arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.08827v1" rel="noopener" target="_blank">Self Rewarding Self Improving</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.08827v1" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 self-evolving curriculum for llm reasoning arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.14970" rel="noopener" target="_blank">Self-Evolving Curriculum for LLM Reasoning</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.14970" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/ServiceNow/sec" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 reflect, retry, reward: self-improving llms via reinforcement learning arxiv" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.24726" rel="noopener" target="_blank">Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2505.24726" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2025 adaptive self-improvement llm agentic system for ml library development icml" data-year="2025">
<td class="year">2025</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2502.02534" rel="noopener" target="_blank">Adaptive Self-improvement LLM Agentic System for ML Library Development</a>
</td>
<td>ICML</td>
<td class="links"><a href="https://arxiv.org/abs/2502.02534" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/zhang677/PCL-lite" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 learning to reason without external rewards iclr" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2505.19590" rel="noopener" target="_blank">Learning to Reason without External Rewards</a>
</td>
<td>ICLR</td>
<td class="links"><a href="https://arxiv.org/abs/2505.19590" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/sunblaze-ucb/Intuitor" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 structured reasoning for large language models arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2601.07180" rel="noopener" target="_blank">Structured Reasoning for Large Language Models</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2601.07180" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 ireasoner: trajectory-aware intrinsic reasoning supervision for self-evolving large multimodal models arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2601.07180" rel="noopener" target="_blank">iReasoner: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2601.07180" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/meghanaasunil/iReasoner" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 strive: structured reasoning for self-improvement in claim verification machine intelligence research" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://link.springer.com/article/10.1007/s11633-025-1598-5" rel="noopener" target="_blank">STRIVE: Structured Reasoning for Self-improvement in Claim Verification</a>
</td>
<td>machine intelligence research</td>
<td class="links"><a href="https://link.springer.com/article/10.1007/s11633-025-1598-5" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 unicorn: towards self-improving unified multimodal models through self-generated supervision arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2601.03193" rel="noopener" target="_blank">UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2601.03193" rel="noopener" target="_blank">Paper</a> · <a href="https://github.com/Hungryyan1/UniCorn" rel="noopener" target="_blank">Code</a></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 retrospective progress-aware self-refinement for llm agent training arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2606.14302" rel="noopener" target="_blank">Retrospective Progress-Aware Self-Refinement for LLM Agent Training</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2606.14302" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
<tr class="paper-row" data-branch="fm" data-search="2026 eve-agent: evidence-verifiable self-evolving agents arxiv" data-year="2026">
<td class="year">2026</td>
<td class="title-cell">
<a href="https://arxiv.org/abs/2605.22905" rel="noopener" target="_blank">EVE-Agent: Evidence-Verifiable Self-Evolving Agents</a>
</td>
<td>arXiv</td>
<td class="links"><a href="https://arxiv.org/abs/2605.22905" rel="noopener" target="_blank">Paper</a> · <span class="muted-link">Code</span></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="library-block" id="experience">
<div class="block-head">
<div>
<h3>1.3 Extrinsic Exploratory Experience</h3>
<p>The agent improves through trajectories gathered from interaction with environments or learned simulators.</p>
</div>
<div class="count-badge">32 papers</div>
</div>
<details class="subgroup" open="">
<summary id="experience-env">
<span>1.3.1 Grounded Executable Environments</span>
<span class="count-badge small">17 papers</span>
</summary>
<p class="sub-desc">Learning from trajectories, rewards, or observations obtained by acting in executable environments such as code runtimes, web interfaces, games, GUI systems, or robotics simulators.</p>
<div class="table-wrap">
<table class="paper-table">
<thead>
<tr><th>Year</th><th>Title</th><th>Venue</th><th>Links</th></tr>
</thead>
<tbody>
<tr class="paper-row" data-branch="fm" data-search="2023 robocat: a self-improving generalist agent for robotic manipulation tmlr" data-year="2023">
<td class="year">2023</td>
<td class="title-cell">