Better Call GRPO

GRPO outperforms SFT for ASR adaptation using only synthetic speech

A new arXiv preprint demonstrates that Group Relative Policy Optimization (GRPO) substantially outperforms supervised fine-tuning (SFT) when adapting LLM-based automatic speech recognition to regulated domains using only synthetic TTS data. GRPO alone reduces word error rate by 40% relative to SFT (36.71% → 22.09%), with an SFT+GRPO combination achieving 45% relative reduction. The authors attribute gains to behavioral changes — improved stopping calibration and better audio-text alignment — rather than representational shifts in early layers.