RASFT is an adaptive SFT method that strengthens or relaxes expert imitation per problem based on on-policy rollout solvability and adds clipped reference-policy ratio to limit drift, reporting better results than standard SFT and RL on math and code benchmarks.
International Conference on Learning Representations , year =
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GAC derives adaptive mixing weights for SFT-RL hybrid post-training from online gradient variance and signal disagreement estimates, improving benchmark performance over fixed schedules with under 1% overhead.
EKSFT masks high-entropy or high-KL tokens in low-data SFT to preserve pre-trained distribution and improve downstream RL performance on math reasoning tasks.
citing papers explorer
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RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning
RASFT is an adaptive SFT method that strengthens or relaxes expert imitation per problem based on on-policy rollout solvability and adds clipped reference-policy ratio to limit drift, reporting better results than standard SFT and RL on math and code benchmarks.
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GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
GAC derives adaptive mixing weights for SFT-RL hybrid post-training from online gradient variance and signal disagreement estimates, improving benchmark performance over fixed schedules with under 1% overhead.