#Grpo
Showing 23 of 23 repositories tagged #grpo, ranked by stars
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成
Solve Visual Understanding with Reinforced VLMs
OpenClaw-RL: Train any agent simply by talking
Implement a reasoning LLM in PyTorch from scratch, step by step
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
Skywork-R1V is an advanced multimodal AI model series developed by Skywork AI, specializing in vision-language reasoning.
The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and monitoring.
🌾 OAT: A research-friendly framework for LLM online alignment, including reinforcement learning, preference learning, etc.
Official implementation of GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale
Code For Adaptive-Boundary-Clipping GRPO. arxiv.org/pdf/2601.03895
Agentic RAG R1 Framework via Reinforcement Learning
LightRFT: Light, Efficient, Omni-modal & Reward-model Driven Reinforcement Fine-Tuning Framework
A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch
OSS RL environment + evals toolkit
[ICLR'26] AutoGEO: a Generative Engine Optimization framework to automatically learn generative engine preferences, and rewrite web contents for more traction.
逐行对照 MiniMind 源码精读、并延伸到大模型技术体系的中文学习笔记 —— 预训练 / SFT / DPO / PPO / GRPO、训练机制、MiniMind2→3 版本对照、真实实验证据。
[ICLR'26] Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
A travel agent based on Qwen2.5, fine-tuned by SFT + DPO/PPO/GRPO using traveling question-answer dataset, a mindmap can be output using the response. A RAG system is build upon the tuned qwen2, using Prompt-Template + Tool-Use + Chroma embedding database + LangChain
Curated, opinionated index of post-R1 LLM × Reinforcement Learning. Many deep-dive blog posts cross-linked to many papers — GRPO, DAPO, DPO, PPO, RLHF, GSPO, CISPO, VAPO, Reward Modeling, MoE RL stability, Verifier-Free RL, Training-Free RL, Agentic RL, DeepSeek-R1 reproduction.
[CVPR 2026 Highlight] ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering