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.
- eval_multi_task: Example for supporting evaluation multiple tasks with different configs.
- 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.
- low_precision: Examples of FP8 training and inference for improved throughput and stability.
- mem_agent: MemAgent long-context RL — chunk-wise memory update, HotpotQA GRPO training, and RULER-HQA evaluation.
- multi_agent: Example of running multi-agent RL with
vime. - tau-bench: Multi-turn tool-use agent training in tau-bench environments.
- train_infer_mismatch_helper: Algorithmic methods for rollout correction (e.g., TIS, MIS).