[ICLR 2026] A Framework for LLM-based Multi-Agent Reinforced Training and Inference
RL environments with verifiable rewards you can trust. Sandboxed coding and computer-use tasks, verifiers kept apart from the answer, a QA gate against reward hacking, and infra failures kept out of training. Works with verl, TRL, OpenRLHF and Axolotl.
Exact-oracle conformance tests for turn-level credit assignment in agentic RL (GRPO, RLOO, GAE, GiGPO; verl, TRL, OpenRLHF).
A list of uv environments templates for LLM development.
RLHF Annotation Studio — Web-based tool for collecting human preference data to train LLMs via Reinforcement Learning from Human Feedback (RLHF). Compare responses side-by-side, capture preferences, and export JSONL for reward model training.
Multi-turn Agent RL training in OpenRLHF, an AgentFlow reimplementation.
🌐 Streamline LLM development with ready-to-use environment templates for efficient setup and deployment.
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