{"id":"hiyouga-easyr1","name":"EasyR1","homepage":"https://verl.readthedocs.io","repo_url":"https://github.com/hiyouga/EasyR1","category":"ai-ml","subcategories":[],"tags":["ai-ml","reinforcement-learning","llm-training","vllm","ray","vision-language-models","lora","distributed-training"],"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.","use_cases":["RLHF/RLAIF-style training of language and vision-language models using GRPO and related algorithms","Multi-modal reinforcement learning over text/vision-text datasets (e.g., VLM reward optimization)","LoRA-based reinforcement learning fine-tuning to reduce GPU memory requirements","Distributed/multi-node RL training using Ray"],"not_for":["Production inference serving (it is a training framework, not an API service)","Teams needing a simple REST/SDK-based integration (the primary interface is CLI/scripts)","Environments that cannot run PyTorch/vLLM/Ray/FlashAttention and associated GPU workloads"],"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.","alternatives":["veRL (original framework)","LlamaFactory (for SFT/inference and some training workflows)","TRL (Hugging Face TRL) for some GRPO-style training approaches (primarily text-focused)","Other RLHF frameworks built around Ray/DeepSpeed/vLLM"],"af_score":42.8,"security_score":43.8,"reliability_score":35.0,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T14:59:09.250716+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":["Python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["No explicit authentication mechanism documented (training-run configuration via environment variables / local credentials for external services like model hubs and loggers)"],"oauth":false,"scopes":false,"notes":"No service/API authentication is described. The README mentions environment variables such as USE_MODELSCOPE_HUB=1 and HF_ENDPOINT for model downloads, and various experiment loggers, but does not document auth flows/scopes for a centralized EasyR1 service."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Open-source framework; costs are primarily compute/GPU and any third-party services used for logging or model hosting."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":42.8,"security_score":43.8,"reliability_score":35.0,"mcp_server_quality":0.0,"documentation_accuracy":70.0,"error_message_quality":null,"error_message_notes":"Troubleshooting guidance exists for a few common runtime issues, but error handling/retry semantics for agent-driven automation are not documented.","auth_complexity":95.0,"rate_limit_clarity":0.0,"tls_enforcement":40.0,"auth_strength":70.0,"scope_granularity":0.0,"dependency_hygiene":55.0,"secret_handling":50.0,"security_notes":"As a local training framework (not a hosted API), it avoids typical server-side auth concerns. Security-relevant risks are mostly indirect: running complex GPU/distributed stacks (vLLM/FlashAttention/Ray/deepspeed-like components) and using external services for model downloads/logging. The README does not describe secret management practices (e.g., preventing logger tokens from being logged), so secret-handling cannot be confirmed. TLS/auth for remote APIs (HF/model hubs/loggers) is not specified here.","uptime_documented":0.0,"version_stability":50.0,"breaking_changes_history":30.0,"error_recovery":60.0,"idempotency_support":"false","idempotency_notes":"Training jobs/checkpoints imply resumability (resume from latest/best checkpoint is mentioned), but no guarantees or explicit idempotency behavior are documented for repeated runs.","pagination_style":"none","retry_guidance_documented":false,"known_agent_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."]}}