These examples provide concrete examples to leverage vime in your own RL workflow. Some examples are just demonstrative, but most of them are verifiable with a concrete performance score.
- coding_agent_rl: End-to-end SWE coding-agent RL: a real coding agent (claude-code) edits code in a per-sample sandbox, and the resulting
git diffis graded against the dataset's test harness. - fully_async: Demonstrates fully asynchronous rollout generation for higher efficiency.
- geo3k_vlm: Training VLMs on a single-turn reasoning task using GRPO on the GEO3K dataset.
- geo3k_vlm_multi_turn: VLM multi-turn training on Geo3k dataset.
- multi_agent: Example of running multi-agent RL with
vime. - train_infer_mismatch_helper: Algorithmic methods for rollout correction (e.g., TIS, MIS).