---

name: core-apis description: "Use and debug EasyR1 DataProto, core algorithm, dynamic batching, logger, and support utility APIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

EasyR1 Core APIs

Use this sub-skill when a task needs EasyR1's low-level support APIs rather than a full training launch or checkpoint conversion workflow.

Read first

• references/api-reference.md: API shapes, signatures, batching semantics, algorithm/loss helpers, dynamic batching, logger/tracker helpers, and checkpoint utility relationships.
• references/troubleshooting.md: fixes for DataProto construction and union errors, chunk/padding mismatches, GRPO grouped-rollout assertions, mask/shape failures, dynamic batching restore mistakes, logger setup issues, and backend limitations.
• scripts/easyr1_dataproto_smoke.py: deterministic CPU smoke check for DataProto split/concat/pad/unpad/repeat, dynamic batching restore, and tiny KL/policy-loss sanity checks.

Routing boundaries

• Use this sub-skill for DataProto, DataProtoItem, DataProtoFuture, tensor/non-tensor batches, padding helpers, batch_collate, core advantage/KL/loss helpers, dynamic sequence-length batching, and logging/tracking support helpers.
• Route training configuration, Ray/vLLM/FSDP launch commands, algorithm override recipes, and full training runtime decisions to the training-workflows sub-skill.
• Route model checkpoint merge/export, Hugging Face conversion, shard inspection, and LoRA merge behavior to the checkpoint-export sub-skill.
• CPU/API checks here do not prove full EasyR1 training readiness; full training still needs a CUDA-oriented EasyR1 runtime with the appropriate flash-attn, vLLM, Ray, model, and dataset stack.

Quick validation

Run the bundled smoke after the EasyR1 package and its CPU API dependencies are importable. From this sub-skill directory:

python scripts/easyr1_dataproto_smoke.py

Expected final line:

easy-r1 core API smoke: success