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-------------------------------------------------------------------------------- 1 | # RL-LLM-NLP 2 | This repository encompasses libraries and papers on Reinforcement Learning (RL) within Large Language Models (LLM) and Natural Language Processing (NLP). 3 | 4 | I consider RL to be a pivotal technology in the field of AI, and NLP (particularly LLM) to be a direction well worth exploring. 5 | 6 | ## Library 7 | 8 | | GitHub | From | Year | Desc | 9 | | ------------------------------------------------------------ | ----------------- | ---- | ------------------------------------------------------------ | 10 | | [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) | PrimeIntellect-ai | 2025 | Prime-rl is a codebase for decentralized RL training at scale. | 11 | | [PRIME](https://github.com/PRIME-RL/PRIME) | PRIME-RL | 2025 | Scalable RL solution for the advanced reasoning of language models | 12 | | [rStar](https://github.com/microsoft/rStar) | MicroSoft | 2025 | | 13 | | [veRL](https://github.com/volcengine/verl) | Bytedance | 2024 | Volcano Engine Reinforcement Learning for LLM | 14 | | [trl](https://github.com/huggingface/trl) | HuggingFace | 2024 | Train LM with RL | 15 | | [RL4LMs](https://github.com/allenai/RL4LMs) | Allen | 2023 | RL library to fine-tune LM to human preferences | 16 | | [alignment-handbook](https://github.com/huggingface/alignment-handbook) | huggingface | 2023 | Robust recipes to align language models with human and AI preferences | 17 | 18 | ## Paper 19 | 20 | | Cate | Abbr | Title | From | Year | Link | 21 | | ------------ | ---------------------- | ------------------------------------------------------------ | --------------------------------- | ---- | ------------------------------------------------------------ | 22 | | RL | MRT | Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning | Carnegie Mellon | 2025 | [paper](http://arxiv.org/abs/2503.07572), [GitHub](https://github.com/CMU-AIRe/MRT) | 23 | | RL | L1, LCPO | L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning | Carnegie Mellon | 2025 | [paper](http://arxiv.org/abs/2503.04697), [GitHub](https://github.com/cmu-l3/l1) | 24 | | RL | Online-DPO-R1 | Online-DPO-R1: Unlocking Effective Reasoning Without the PPO Overhead | Salesforce AI Research | 2025 | [paper](https://efficient-unicorn-451.notion.site/Online-DPO-R1-Unlocking-Effective-Reasoning-Without-the-PPO-Overhead-1908b9a70e7b80c3bc83f4cf04b2f175), [GitHub](https://github.com/RLHFlow/Online-DPO-R1) | 25 | | RL | orz | Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model | StepFun | 2025 | [paper](https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero/blob/main/ORZ_paper.pdf), [GitHub](https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero/tree/main) | 26 | | RL | OREAL | Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning | InternLM | 2025 | [paper](https://arxiv.org/abs/2502.06781), [GitHub](https://github.com/InternLM/OREAL) | 27 | | RL | R1 | DeepSeek-R1 | DeepSeek | 2025 | [paper](https://github.com/deepseek-ai/DeepSeek-R1), ① | 28 | | | | | | | | 29 | | o1 | Sky-T1 | Sky-T1: Train your own O1 preview model within $450 | NovaSky-AI | 2025 | [GitHub](https://github.com/NovaSky-AI/SkyThought) | 30 | | o1 | STILL | A series of technical report on Slow Thinking with LLM | RUCAIBox | 2025 | [GitHub](https://github.com/RUCAIBox/Slow_Thinking_with_LLMs) | 31 | | | | | | | | 32 | | RL Scaling | RM | Inference-Time Scaling for Generalist Reward Modeling | DeepSeek | 2025 | [paper](https://arxiv.org/abs/2504.02495) | 33 | | RL Scaling | LIMR | LIMR: Less is More for RL Scaling | GAIR-NLP | 2025 | [paper](https://arxiv.org/abs/2502.11886), [GitHub](https://github.com/GAIR-NLP/LIMR) | 34 | | RL Scaling | DeepScaleR | DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL | Agentica | 2025 | [paper](https://pretty-radio-b75.notion.site/DeepScaleR-Surpassing-O1-Preview-with-a-1-5B-Model-by-Scaling-RL-19681902c1468005bed8ca303013a4e2), [GitHub](https://github.com/agentica-project/deepscaler) | 35 | | RL Scaling | ScalingLaw | Value-Based Deep RL Scales Predictably | Berkeley | 2025 | [paper](https://arxiv.org/abs/2502.04327) | 36 | | | | | | | | 37 | | SLM | PRIME | Process Reinforcement through Implicit Rewards | PRIME-RL | 2025 | [paper](https://curvy-check-498.notion.site/Process-Reinforcement-through-Implicit-Rewards-15f4fcb9c42180f1b498cc9b2eaf896f), [GitHub](https://github.com/PRIME-RL/PRIME) | 38 | | SLM | rStar-Math | rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking | MicroSoft | 2025 | [paper](https://arxiv.org/abs/2501.04519), [GitHub](https://github.com/microsoft/rStar) | 39 | | SLM | rStar | rStar: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers | MicroSoft | 2024 | [paper](https://arxiv.org/pdf/2408.06195), [GitHub](https://github.com/zhentingqi/rStar) | 40 | | | | | | | | 41 | | Unlearn | | A Closer Look at Machine Unlearning for Large Language Models | Sea AI | 2024 | [paper](https://arxiv.org/abs/2410.08109v1), [GitHub](https://github.com/sail-sg/closer-look-LLM-unlearning) | 42 | | Unlearn | Quark | Quark: Controllable Text Generation with Reinforced [Un]learning | Allen | 2022 | [paper](http://arxiv.org/abs/2205.13636), [GitHub](https://github.com/GXimingLu/Quark) | 43 | | | | | | | | 44 | | Align | ReMax | ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models | CUHK | 2024 | [paper](https://arxiv.org/abs/2310.10505) | 45 | | Align | | A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More | Salesforce | 2024 | [paper](https://arxiv.org/abs/2407.16216) | 46 | | Align | | Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback | Allen | 2024 | [paper](https://arxiv.org/abs/2406.09279), [GitHub](https://github.com/hamishivi/EasyLM) | 47 | | Align | | Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey | Capital One | 2024 | [paper](http://arxiv.org/abs/2409.11564) | 48 | | Align | RLHF | Training language models to follow instructions with human feedback | OpenAI | 2022 | [paper](https://arxiv.org/abs/2203.02155) | 49 | | Align | NLPO | Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization | Allen | 2022 | [paper](http://arxiv.org/abs/2210.01241), [GitHub](https://github.com/allenai/rl4lms) | 50 | | Align | FTHP | Fine-Tuning Language Models from Human Preferences | OpenAI | 2020 | [paper](http://arxiv.org/abs/1909.08593), [GitHub](https://github.com/openai/lm-human-preferences) | 51 | | Align | RLOO | Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs | Cohere | 2024 | [paper](https://arxiv.org/abs/2402.14740) | 52 | | | | | | | | 53 | | | | | | | | 54 | | Optimization | DCPO | DCPO: Dynamic Clipping Policy Optimization | Baichuan | 2025 | [paper](https://arxiv.org/abs/2509.02333), [GitHub](https://github.com/lime-RL/DCPO) | 55 | | Optimization | OPO | On-Policy RL with Optimal Reward Baseline | MicroSoft | 2025 | [paper](https://arxiv.org/abs/2505.23585), [GitHub](https://verl.readthedocs.io/en/latest/algo/opo.html) | 56 | | Optimization | SRPO | SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM | Kuaishou | 2025 | [paper](https://arxiv.org/abs/2504.14286), [Huggingface](https://huggingface.co/Kwaipilot/SRPO-Qwen-32B) | 57 | | Optimization | GMPO | Geometric-Mean Policy Optimization | UCAS, MicroSoft | 2025 | [paper](https://arxiv.org/abs/2507.20673), [GitHub](https://github.com/callsys/GMPO) | 58 | | Optimization | GSPO | Group Sequence Policy Optimization | Qwen | 2025 | [paper](https://arxiv.org/abs/2507.18071) | 59 | | Optimization | GiGPO | Group-in-Group Policy Optimization for LLM Agent Training | Nanyang Technological, Skywork AI | 2025 | [paper](https://arxiv.org/abs/2505.10978), [GitHub](https://github.com/langfengQ/verl-agent) | 60 | | Optimization | CISPO | MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention | MiniMax | 2025 | [paper](https://arxiv.org/abs/2506.13585), [GitHub](https://github.com/MiniMax-AI/MiniMax-M1) | 61 | | Optimization | VAPO | VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks | ByteDance Seed | 2025 | [paper](https://arxiv.org/abs/2504.05118) | 62 | | Optimization | Dr. DAPO | Understanding R1-Zero-Like Training: A Critical Perspective | Sea AI Lab | 2025 | [paper](http://arxiv.org/abs/2503.20783), [GitHub](https://github.com/sail-sg/understand-r1-zero) | 63 | | Optimization | DAPO | DAPO: An Open-Source LLM Reinforcement Learning System at Scale | ByteDance Seed | 2025 | [paper](https://arxiv.org/abs/2503.14476), [GitHub](https://github.com/BytedTsinghua-SIA/DAPO) | 64 | | Optimization | GRPO | DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models | DeepSeek | 2024 | [paper](https://arxiv.org/abs/2402.03300) | 65 | | Optimization | DPO | Direct Preference Optimization: Your Language Model is Secretly a Reward Model | Stanford | 2024 | [paper](https://arxiv.org/abs/2305.18290) | 66 | | Optimization | DT | Decision Transformer: Reinforcement Learning via Sequence Modeling | Berkeley | 2021 | [paper](https://arxiv.org/abs/2106.01345), [GitHub](https://github.com/kzl/decision-transformer) | 67 | | Optimization | PPO | Proximal Policy Optimization Algorithms | OpenAI | 2017 | [paper](https://arxiv.org/abs/1707.06347) | 68 | | Optimization | REINFORCE multi-sample | Buy 4 Reinforce Samples, Get a Baseline for Free! | University of Amsterdam | 2019 | [paper](https://openreview.net/pdf?id=r1lgTGL5DE) | 69 | | Optimization | REINFORCE | Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning | Northeastern University | 1992 | [paper](https://people.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf) | 70 | 71 | ## Appendix 72 | 73 | - ① DeepSeek-R1相关复现： 74 | - [Jiayi-Pan/TinyZero: Clean, minimal, accessible reproduction of DeepSeek R1-Zero](https://github.com/Jiayi-Pan/TinyZero) 75 | - [huggingface/open-r1: Fully open reproduction of DeepSeek-R1](https://github.com/huggingface/open-r1) 76 | - [hkust-nlp/simpleRL-reason: This is a replicate of DeepSeek-R1-Zero and DeepSeek-R1 training on small models with limited data](https://github.com/hkust-nlp/simpleRL-reason) 77 | - [ZihanWang314/RAGEN: RAGEN is the first open-source reproduction of DeepSeek-R1 on AGENT training.](https://github.com/ZihanWang314/ragen) 78 | --------------------------------------------------------------------------------