An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models

[EMNLP'25] s3 - ⚡ Efficient & Effective Search Agent Training via RL for RAG (Verifier-Powered RLVR for Search)

An Awesome List of Agentic Model trained with Reinforcement Learning

Official repository for "RLVR-World: Training World Models with Reinforcement Learning" (NeurIPS 2025), https://arxiv.org/abs/2505.13934

A curated list of awesome resources about reward construction for AI agents. This repository covers cutting-edge research, and practical guides on defining and collecting rewards to build more intelligent and aligned AI agents.

a Python library that uses Reinforcement Learning (RL) to train LLMs.

🐝 SwarmBench: Benchmarking LLMs' Swarm Intelligence

grpo to train long form QA and instructions with long-form reward model

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs

procedural reasoning datasets

Enemies for your LLM

A curated collection of research papers on LLM Tool-Integrated Reasoning (TIR), where LLMs enhance reasoning by interacting with external tools such as calculators, search engines, and code interpreters.

CAP RLVR: Reinforcement Learning from Human Feedback for Legal Reasoning using Caselaw Access Project data. Complete GRPO training pipeline with OpenAI Gym environments, deterministic reward functions, and multi-stage curriculum learning for legal LLM development.

Finetuning of Qwen3 0.6B for MCQA tasks

RLHF and Verifiable Reward Models - Post training Research

A composable component orchestrator for Reinforcement Learning from Verifiable Rewards (RLVR) training of Large Language Models on reasoning tasks.

A curated list of papers on implicit-reward reinforcement learning for LLMs — no human feedback, no gold answers, no verifiable rewards.

Profile for Jonathan Rahn — AI Lab Lead at Drees & Sommer. Chess-reasoning LMs (policy + world model), RL with verifiable rewards.

A curated collection of papers combining Self-Supervised Learning (SSL) with Reinforcement Learning (RL) in the context of Large Language Models (LLMs), toward autonomous agents in the Era of Experience. Inspired by “Welcome to the Era of Experience” (Silver & Sutton, 2025).