awesome-adaptation-agentic-ai

name	awesome-adaptation-agentic-ai
description	Curated research collection on adaptation strategies for agentic AI systems, covering agent and tool adaptation methods with RL, SFT, and DPO approaches
triggers	["find papers on agentic AI adaptation","how do AI agents adapt their tool use","research on tool execution adaptation","agent reinforcement learning methods","lookup agentic AI adaptation techniques","show me agent adaptation frameworks","what are tool adaptation strategies","find research on adaptive AI agents"]

Awesome Adaptation of Agentic AI

Skill by ara.so — AI Agent Skills collection.

Overview

This repository is a curated research collection accompanying the paper "Adaptation of Agentic AI" (arXiv:2512.16301). It systematically organizes papers on how AI agents adapt their behavior, particularly focusing on:

• Agent Adaptation: Methods to improve agent decision-making through tool execution signals (A1) or output signals (A2)
• Tool Adaptation: Approaches to optimize tools either independently (T1) or with agent supervision (T2)

The collection categorizes 40+ research papers by adaptation strategy, training method (RL, SFT, DPO), task domain, and implementation details.

Repository Structure

awesome-adaptation-of-agentic-ai/
├── README.md                 # Main paper collection with categorized tables
├── images/
│   ├── intro.png            # Overview diagram
│   ├── a1_illustrate.png    # Tool execution signaled adaptation
│   ├── a1_timeline.png      # Development timeline
│   ├── paper_icon.png       # Paper link icons
│   └── code_icon.png        # Code link icons
└── LICENSE

Installation & Usage

Cloning the Repository

# Clone the repository
git clone https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI.git
cd Awesome-Adaptation-of-Agentic-AI

# View the main README
cat README.md

Browsing Papers

The repository organizes papers into four main categories:

1. A1: Tool Execution Signaled Agent Adaptation

   • RL-based methods (GRPO, PPO, AlphaZero-like)
   • SFT & DPO methods

2. A2: Agent Output Signaled Adaptation

   • Coming in future updates

3. T1: Agent-Agnostic Tool Adaptation

   • Coming in future updates

4. T2: Agent-Supervised Tool Adaptation

   • Coming in future updates

Key Research Categories

A1: Tool Execution Signaled (RL-Based)

Papers where agents learn from tool execution feedback using reinforcement learning:

Notable Methods:

• Orion (2025.11): IR agents with GRPO on LFM2
• DeepSeek-R1-Zero (2025.01): Coding agents with code executor feedback
• DeepSeek-Prover-V2 (2025.04): Formal theorem proving with Lean compiler
• FTRL (2025.08): Multi-step tool-use with GRPO

Common Pattern:

Agent → Tool Call → Execution Feedback → RL Update (GRPO/PPO)

A1: Tool Execution Signaled (SFT & DPO)

Papers using supervised fine-tuning and direct preference optimization:

Notable Methods:

• ToolLLM (2023.07): API planning with real-world APIs
• RetPO (2024.02): Information retrieval with DPO
• AWL (2024.12): Scientific reasoning with adaptive learning

Common Pattern:

Agent → Tool Call → Execution Trace → Supervised Learning

Common Use Cases

Finding Papers by Task

Example: Formal Theorem Proving

# Search README for theorem proving papers
grep -i "theorem proving" README.md

Papers include: AlphaProof, BFS-Prover-V2, Goedel-Prover-V2, Leanabell-Prover-V2, DeepSeek-Prover-V1.5/V2

Example: Coding & Code Execution

# Find coding-related papers
grep -i "coding\|code executor" README.md

Papers include: olmOCR2, R1-Code-Interpreter, Code-R1, DeepSeek-R1-Zero, RLEF, LeDex, CYCLE, CodeAct

Finding Papers by Method

Example: GRPO (Group Relative Policy Optimization)

# List all GRPO papers
grep "GRPO" README.md

Commonly used in: Tool-N1, DeepSeek-Prover-V2, SQL-R1, Rec-R1, DeepRetrieval, etc.

Example: DPO (Direct Preference Optimization)

# List all DPO papers
grep "DPO" README.md

Used in: AWL, LeReT, TP-LLaMA, RetPO

Finding Papers by Model Backbone

Example: Qwen2.5-based agents

# Find Qwen2.5 implementations
grep "Qwen2.5" README.md

Models include: olmOCR2, ToolExpander, BFS-Prover-V2, WebGen-Agent, Tool-R1, etc.

Accessing Paper Resources

Paper Links

All papers include arXiv or conference links:

[Paper](https://arxiv.org/abs/2512.16301)

Code Repositories

Many papers provide implementation code:

[Code](https://github.com/example/repo)

Reading Strategy

# Pseudo-code for systematic review
def research_adaptation_strategy(task_domain, method_type):
    """
    Navigate to specific adaptation category
    
    Args:
        task_domain: e.g., "coding", "theorem proving", "IR"
        method_type: "RL", "SFT", "DPO"
    
    Returns:
        List of relevant papers with links
    """
    # 1. Go to appropriate section (A1, A2, T1, T2)
    # 2. Filter by method_type (RL-based vs SFT/DPO)
    # 3. Search table for task_domain
    # 4. Check paper links, code availability, model backbones
    pass

Citation Format

When using this repository for research:

@article{jiang2025adaptation,
  title={Adaptation of Agentic AI},
  author={Jiang, Pengcheng and Lin, Jiacheng and Shi, Zhiyi and Wang, Zifeng and He, Luxi and Wu, Yichen and Zhong, Ming and Song, Peiyang and Zhang, Qizheng and Wang, Heng and others},
  journal={arXiv preprint arXiv:2512.16301},
  year={2025}
}

Contributing

The repository welcomes pull requests for:

• New papers on agentic AI adaptation
• Updates to existing paper information
• Corrections to categorizations
• Additional metadata (benchmarks, datasets)

Contribution Pattern:

# 1. Fork the repository
# 2. Add paper to appropriate category in README.md
# 3. Follow existing table format:
# | Time | Method | Venue | Task(s) | Tool(s) | Agent Backbone | Tuning |

# Example entry:
# | 2025.XX | YourMethod | Venue<br>🔗 [Paper](link)<br>💻 [Code](link) | Task | Tools | Model | Method |

# 4. Submit pull request

Key Insights from the Collection

Adaptation Taxonomy

1. Signal Type:

   • Tool execution feedback (A1)
   • Agent output quality (A2)

2. Training Methods:

   • GRPO: Group Relative Policy Optimization (most common in 2025)
   • PPO: Proximal Policy Optimization
   • AlphaZero-like: Self-play with value/policy networks
   • SFT: Supervised fine-tuning on execution traces
   • DPO: Direct preference optimization

3. Trend: Growing use of GRPO for tool-augmented agents (2025), especially with Qwen2.5 and DeepSeek models

Domain Coverage

• Coding: Code execution sandboxes (DeepSeek-R1-Zero, R1-Code-Interpreter)
• Formal Math: Lean compilers (AlphaProof, DeepSeek-Prover)
• Information Retrieval: Search engines, retrievers (DeepRetrieval, ReZero)
• Tool-Calling: API execution (ToolLLM, Tool-N1, ToolExpander)
• Web Agents: GUI interaction (WebGen-Agent)

Troubleshooting

Finding Specific Research

Q: How do I find papers using a specific model like LLaMA3?

grep -i "llama3" README.md

Q: Which papers have open-source code?

# Look for code icon in tables
grep "code_icon.png" README.md

Q: What are the most recent papers?

# Check Time column (sorted by date within categories)
# Most recent: 2025.11 (Orion), 2025.10 (olmOCR2, AlphaProof)

Understanding Abbreviations

• IR: Information Retrieval
• GRPO: Group Relative Policy Optimization
• PPO: Proximal Policy Optimization
• SFT: Supervised Fine-Tuning
• DPO: Direct Preference Optimization
• TTRL: Test-Time Reinforcement Learning
• EI: Expert Iteration

Repository Maintenance

The repository is actively maintained with:

• Last Update: 2026-05-15 (per metadata)
• Stars: 650+ (growing at ~3 stars/day)
• Open Issues: 2

For questions or issues, check: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI/issues

Related Resources

• Homepage: https://arxiv.org/abs/2512.16301
• License: CC BY-NC-ND 4.0 (content), NOASSERTION (code)
• Topics: adaptation, agentic-ai, large-language-models