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Filtering out fit sequences during fine-tuning can boost post-training performance by up to 17%, reshaping how we approach model training for RL applications.
Understanding how diverse SFT data can activate strategy selection reveals new pathways for enhancing model reasoning capabilities through targeted post-training interventions.
