{"id":"hiyouga-easyr1","name":"EasyR1","af_score":42.8,"security_score":43.8,"reliability_score":35.0,"what_it_does":"EasyR1 is an open-source RL training framework (a fork of veRL) for efficient, scalable reinforcement learning (e.g., GRPO/DAPO/Reinforce++/ReMax/RLOO/GSPO/CISPO) on text and vision-language (multi-modality) models, leveraging vLLM (SPMD mode), FlashAttention, and Ray for multi-node scaling. It provides training scripts, dataset formatting guidance, and checkpoint utilities for model merging/export.","best_when":"You have GPU infrastructure and want to train or continue training RL-based policies for LLMs or VLMs using the supported algorithms (and are comfortable running the provided example scripts).","avoid_when":"You need a managed SaaS with service-level guarantees, a stable HTTP API with rate limits, or you cannot accommodate the heavy ML stack dependencies.","last_evaluated":"2026-03-29T14:59:09.250716+00:00","has_mcp":false,"has_api":false,"auth_methods":["No explicit authentication mechanism documented (training-run configuration via environment variables / local credentials for external services like model hubs and loggers)"],"has_free_tier":false,"known_gotchas":["No MCP/REST interface; automation requires running training scripts/CLIs and managing environment and dependencies.","Vision-language training can fail due to token/feature length mismatches (e.g., max_prompt_length/max_pixels issues).","GPU OOM is a common failure mode; requires tuning GPU utilization/offload settings.","Distributed training depends on correct Ray/deepspeed driver environment; misconfiguration can yield runtime failures."],"error_quality":null}