{"id":"openrlhf-openrlhf","name":"OpenRLHF","af_score":49.2,"security_score":22.2,"reliability_score":32.5,"what_it_does":"OpenRLHF is an open-source RLHF framework for training and improving language models using reinforcement learning from human feedback. It provides distributed RL training (e.g., PPO, REINFORCE++, GRPO, RLOO) built around Ray orchestration and vLLM-based fast sample generation, with support for multi-turn agent-based execution and integration with HuggingFace/DeepSpeed for large-model training.","best_when":"You have GPU/distributed infrastructure and want to run RLHF training pipelines (including sample generation throughput) with Ray + vLLM and optionally DeepSpeed/Transformers for large models.","avoid_when":"You need a lightweight library with minimal infrastructure, or you require a stable, versioned HTTP API surface for integration by external clients.","last_evaluated":"2026-03-29T14:55:11.017151+00:00","has_mcp":false,"has_api":false,"auth_methods":["Configuration-based auth for remote reward models / agent servers (e.g., --remote_rm_url, --agent_func_path); no first-party auth spec in provided data"],"has_free_tier":false,"known_gotchas":["Heavier-than-typical integration: operates as a distributed training framework (Ray actors, vLLM engines, DeepSpeed), not a simple request/response API.","Multi-turn agent execution depends on correct environment reset/step semantics or external agent server behavior; integration mistakes can silently degrade training.","Throughput/performance tuning requires careful configuration (e.g., vLLM engine counts, tensor/pipeline parallelism), and small misconfigurations can cause instability or poor utilization."],"error_quality":0.0}