The examples under recipes/ are representative extensions to verl for specific end-to-end RL training recipes.
- s3 Efficient Yet Effective Search Agent Training via RL
- Rec-R1: Bridging Generative Large Language Models and Recommendation Systems via Reinforcement Learning
- Explore RL Data Scaling: Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
- FIRE: Flaming-hot initiation with regular execution sampling for large language models
- DQO: Enhancing multi-Step reasoning abilities of language models through direct Q-function optimization
- cognition-engineering: Test time scaling drives cognition engineering.
- Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning.
- AdaRFT: Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
- critic-rl: LLM critics for code generation
- self-rewarding-reasoning-LLM: self-rewarding and correction with generative reward models
- DeepEnlighten: Reproduce R1 with social reasoning tasks and analyze key findings
- MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
- PURE: Credit assignment is the key to successful reinforcement fine-tuning using process reward model
- cognitive-behaviors: Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
- deepscaler: iterative context scaling with GRPO
- DAPO: the fully open source SOTA RL algorithm that beats DeepSeek-R1-zero-32B