alphaXiv(@askalphaxiv):"Finetuning with Sampling: SFT Learns Better Than You Think" SFT may be better than we think, and the problem could have just been the data distribution. This paper uses MCMC to rewrite expert traces into equally correct answers that look more like what the base model would naturally produce. With that, plain SFT can outperform RL while forgetting less!

2026.10.04 15:00

"Finetuning with Sampling: SFT Learns Better Than You Think" SFT may be better than we think, and the problem could have just been the data distribution. This paper uses MCMC to rewrite expert traces into equally correct answers that look more like what the base model would naturally produce. With that, plain SFT can outperform RL while forgetting less!