# HHHHHejia/Awesome-AgenticLLM-RL-Papers

Repository: https://github.com/HHHHHejia/Awesome-AgenticLLM-RL-Papers
Canonical: https://ross.abutalabs.com/products/awesome-agenticllm-rl-papers
License Family: other
Last push: 2026-06-18T07:12:43+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 26
- inputs: {"age_days": 369, "days_push": 76, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1874, forks 85 (observed 2026-08-28T04:05:47.384389+00:00)

## What it is
A curated paper list accompanying the survey 'The Landscape of Agentic Reinforcement Learning for LLMs', cataloguing RL algorithms (PPO, DPO families and beyond) for agentic LLM training. It organizes methods by objective type, mechanisms, and links to papers and code.

## Use cases
- find papers on reinforcement learning for LLM agents
- survey of agentic RL algorithms like PPO and DPO
- research reading list for LLM RL training
- compare RLHF and preference optimization methods
- locate code implementations of agentic RL algorithms
- background reading for training LLM agents with RL

## When to choose
- you need a curated, survey-backed reading list on agentic RL for LLMs
- you want quick comparisons of RL algorithm variants with paper and code links

## When to avoid
- you need runnable training code rather than a paper index
- you want a tool or library rather than research references

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, reinforcement-learning, llm-training, agent-framework
- domain: reinforcement-learning, large-language-models, machine-learning, tutorials, awesome-lists
- platform: -
- tags: survey, papers, awesome-list, agentic-rl, ppo, dpo, research, ai-agents, web-server

## Member repositories
- HHHHHejia/Awesome-AgenticLLM-RL-Papers (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:47.384389+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:14:47.327906+00:00, confidence not recorded.
  - readme: https://github.com/HHHHHejia/Awesome-AgenticLLM-RL-Papers (fetched 2026-08-28T04:05:47.384389+00:00, sha 774ec0883017)
- Data as of 2026-08-30T08:39:29.467469+00:00.