Alignment Under Heat: GANPO Targets the Fragility That Benchmarks Miss
TL;DR for operators A preference-tuned model can look stable under ordinary evaluation and become less reliable once production decoding introduces more randomness. That gap is the main reason to pay attention to GANPO. The paper’s standard alignment gains are real but not large. On length-controlled AlpacaEval, adding GANPO raises DPO from 27.79 to 29.69 for Gemma2-2B-it and from 32.34 to 33.87 for Llama3-8B-Instruct. The SimPO gains are similarly modest: 36.03 to 36.74 and 48.31 to 50.48. Response length stays essentially unchanged. ...