Fine-tune a small reasoning model with GRPO + LoRA using a rank-8/alpha-16, no-value-network recipe

Unsloth Documentation·medium signal

GRPO samples multiple completions per prompt and optimizes on group-normalized relative advantages, eliminating the separate value network that PPO needs — and it composes cleanly with LoRA by freezing all weights except rank-8 (alpha-16) adapters, with gradient clipping and within-batch advantage standardization for stability. This lets a solo builder do R1-style chain-of-thought RL on a single consumer GPU against a verifiable-reward dataset like GSM8K, rather than reaching for full-parameter RLHF.