These examples provide concrete examples to leverage slime 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.
- multi_agent: Example of running multi-agent RL with
slime. - on_policy_distillation: Example implementation for on-policy distillation, extending the reinforcement learning pipeline to support teacher–student distillation directly within on-policy training.
- reproducibility: Guides on achieving bitwise experiment reproduction using deterministic modes.
- retool: Demonstrates the retool functionality for tool-enabled language model generation.
- search-r1: A minimal reproduction of Search-R1, featuring multi-turn conversation and tool-calling.
- strands_sglang: Integration example with the Strands-Agents scaffolding framework.
- tau-bench: Training in an agentic multi-turn tool use environment (Tau-bench).
- train_infer_mismatch_helper: Algorithmic methods for rollout correction (e.g., TIS, MIS).