Grpo Rlvr Training

Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.

GRPO & RLVR Training

This skill assumes finetuning-method-selection already routed here because the target behavior has a verifiable pass/fail signal — not demonstrations (lora-qlora-recipes) or preference pairs (preference-optimization). What follows is when RL is the right tool, the reference recipe, the mandatory reward-inspection gate, and how to pick a GRPO variant when the base recipe misbehaves.

Input: a routing decision (RLVR via GRPO) plus a verifier (code executor, test suite, schema checker, or grader) for the target task. Output format: a validated GRPO config — the kwarg values in references/grpo-memory.md and the reward functions in references/reward-functions.md, not free-form advice — that llm-finetuning-training-engineer consumes directly.

When RL Applies

GRPO+RLVR only pays off when task success is algorithmically checkable — a unit test passes, a parser accepts the output, a tool call matches an expected schema, a math answer matches a ground truth. If grading the output requires human judgment or a subjective rubric, that's an eval-harness and judge-calibration problem first — see — not a reason to skip straight to RL.

Failure mode	Variant	Why
Entropy collapse / degenerate long chain-of-thought	DAPO	Decouples clip bounds and relaxes the KL penalty that over-regularizes exploration on long reasoning traces
Reward or output length trends up regardless of quality	Dr.GRPO	Removes GRPO's length-normalization bias so reward tracks correctness, not completion length
Training a mixture-of-experts model	GSPO	Moves the importance-sampling ratio to the sequence level instead of per-token — per-token ratios are unstable on MoE routing, so GSPO is required here, not optional

Grpo Rlvr Training

GRPO & RLVR Training

When RL Applies

The Recipe

The Inspection Rule

Variant Selection

VLM RL Is Reference-Only

References