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.

name: grpo-rlvr-training description: 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 eval-harness-first — not a reason to skip straight to RL.

Before opening a GRPO run, confirm the model can sometimes succeed on the target task already. RL sharpens an existing capability by reweighting toward the samples that already work; it does not install a capability from zero.

The model never succeeds, even at low temperature across many samples: the gap is format or task understanding, not policy refinement. Route back to SFT first (lora-qlora-recipes) and only return to this skill once the base success rate is nonzero.
The model succeeds sometimes, inconsistently: this is the GRPO sweet spot — proceed to The Recipe below.

The standing rule for the whole plugin: DPO for taste, GRPO for reasoning. If the signal is a preference between two acceptable outputs, that's preference-optimization, not this skill.

The Recipe

The reference recipe is TRL's GRPOTrainer with vLLM-backed generation:

from trl import GRPOConfig, GRPOTrainer

grpo_args = GRPOConfig(
    output_dir="./outputs-grpo",
    use_vllm=True,
    vllm_mode="colocate",       # single GPU; "server" for multi-GPU
    num_generations=8,          # floor — fewer starves the group-relative baseline
    learning_rate=5e-7,         # settled range for GRPO
    beta=0.01,                  # KL coefficient vs the reference policy
    per_device_train_batch_size=8,
    gradient_accumulation_steps=4,
    bf16=True,
    logging_steps=10,
    seed=3407,
)

trainer = GRPOTrainer(
    model=SFT_CHECKPOINT,
    args=grpo_args,
    reward_funcs=[format_reward, correctness_reward],   # references/reward-functions.md
    train_dataset=prompts,       # prompt-only — GRPO generates its own completions
    processing_class=tokenizer,
)

trainer.train()

vllm_mode="colocate" runs generation and training on the same GPU — the default for a single-GPU box.
vllm_mode="server" points at a separate vLLM server process and is the multi-GPU path — generation and training don't compete for the same device.
num_generations ≥ 8 is a floor, not a suggestion: GRPO's advantage estimate is relative to the group mean, and fewer than 8 samples per prompt produces a noisy baseline.
Reward is composite — a format reward (did the output parse / match the required structure) plus a correctness reward (did the answer verify). A well-formed-but-wrong answer and a malformed one should not score identically; correctness alone loses that signal.
learning_rate=5e-7 and beta=0.01 are the settled starting point; deviate only after the base run is stable and reward-inspected (below).

Memory sizing for this recipe by target size class: references/grpo-memory.md.

The Inspection Rule

Run the reward function against 50–100 sampled outputs and manually read the results before starting the actual training run. This is a gate, not a one-time sanity check.

If the reward function's judgment disagrees with a human reading of that sample, fix the reward function first. Training against an uninspected reward, or tuning hyperparameters to compensate for one silently scoring the wrong thing, is how a run reward-hacks: the model optimizes cleanly toward the wrong target, and that doesn't surface as a training-loop bug.

This inspection is a Phase 1 gate input for /finetune — the same 50–100-sample read that catches a broken reward function here is what that command checks for before it lets a GRPO brief proceed.

Complete reward function implementations to inspect against — exact-match, schema-validation, unit-test-execution, a length-penalty wrapper, and a rubric-as-reward judge pattern: references/reward-functions.md.

Variant Selection

The base recipe above is the default. Reach for a variant only when a specific failure mode shows up, not preemptively:

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

Start with plain GRPO. Watch for the specific symptom — collapsing entropy on long CoT, a length-reward correlation, or MoE instability — and only then swap in the matching variant above. Don't pre-select a variant before the base recipe has actually shown the failure mode.

VLM RL Is Reference-Only

Vision-language RL is not executed by this plugin in v1 — it's documented here for context, not as a runnable path. Tooling is fragmented across ms-swift and EasyR1-derived forks with no one-line TRL command yet, and naive text-only GRPO applied to a VLM tends to reward-hack by optimizing the text-reasoning trace while ignoring the image — the model learns to sound right without looking at the input. A VLM RL run is a research spike outside this skill's supported recipe, not a variant of The Recipe above.

References

references/reward-functions.md — complete Python reward functions (exact-match correctness, schema validation, unit-test execution, a length-penalty wrapper, and a rubric-as-reward judge pattern) to inspect under The Inspection Rule before any training run.
references/grpo-memory.md — memory sizing by target size class, vLLM sleep-mode and optimizer-state tactics, Unsloth's long-context RL chunking, and the DGX Spark bandwidth caveat for decode-heavy rollouts.

Related skills: finetuning-method-selection routes here once a verifiable pass/fail signal exists; preference-optimization is the sibling skill for preference pairs rather than verifiable rewards; eval-harness-first covers judge calibration for any reward that isn't purely code-checkable. On DGX Spark, defer to the dgx-spark-ops plugin's skills, when installed, for the memory/thermal remediation ladder this skill's memory table doesn't cover.