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STEPS is proposed, a selective OPSD framework that controls the location, direction, and duration of distillation that improves mean accuracy over GRPO and support brief, targeted self-distillation as an efficient complement to reward-driven reasoning training.
RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substantial gains, we find that they suffer from exploration bias towards easy instructions when the training data has multiple instructions in a prompt. This bias is caused by two...
CacheBridge is introduced, which co-designs architecture-indexed mapper support, attention-aligned calibration, and bounded mapper construction while retaining a closed-form affine interface for online deployment.
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