VERL is a reinforcement-learning–oriented toolkit designed to train and align modern AI systems, from language models to decision-making agents. It brings together supervised fine-tuning, preference modeling, and online RL into one coherent training stack so teams can move from raw data to aligned policies with minimal glue code. The library focuses on scalability and efficiency, offering distributed training loops, mixed precision, and replay/buffering utilities that keep accelerators busy. It ships with reference implementations of popular alignment algorithms and clear examples that make it straightforward to reproduce baselines before customizing. Data pipelines treat human feedback, simulated environments, and synthetic preferences as interchangeable sources, which helps with rapid experimentation. VERL is meant for both research and production hardening: logging, checkpointing, and evaluation suites are built in so you can track learning dynamics and regressions over time.
Features
- Unified pipeline for SFT, preference modeling, and online RL
- Distributed training with mixed precision and efficient replay buffers
- Reference implementations of popular alignment/RL algorithms
- Pluggable data sources for human, simulated, and synthetic feedback
- Comprehensive logging, checkpoints, and eval dashboards
- Extensible components for custom rewards, policies, and environments
Project Samples
