TOPD improves on-policy distillation for LLM reasoning by using near-future guidance to identify divergent states, raising average accuracy from 47.8% to 52.2% on math benchmarks including AIME24 and AIME25.
Srkd: Towards efficient 3d point cloud segmentation via structure-and relation-aware knowledge distillation
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Rock Tokens in on-policy distillation persist at high loss, account for up to 18% of outputs, absorb large gradient norms, but add negligible value to reasoning performance.
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.
citing papers explorer
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Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
TOPD improves on-policy distillation for LLM reasoning by using near-future guidance to identify divergent states, raising average accuracy from 47.8% to 52.2% on math benchmarks including AIME24 and AIME25.
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Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
Rock Tokens in on-policy distillation persist at high loss, account for up to 18% of outputs, absorb large gradient norms, but add negligible value to reasoning performance.
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Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.