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ARXIV:2606.03532 · LLM TRAINING · SUBMITTED 03 JUN · 20:42 UTC · FRESHNESS FRESH
ARXIV:2606.03532LLM TRAININGSUBMITTED 03 JUN · 20:42 UTCFRESHNESS FRESHHaowei Guo · Baolong Bi · Ruicheng Zhang · Bingqian Sun · Wentao Zhang · arXiv
Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks.
Opportunity summary
Pain Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks. Through a controlled schedule sweep on Qwen3-8B, we establish…
Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal coupling} between teacher and student -- has not…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. With a single shared parameter set and no per-dataset retuning, CGTR achieves \textbf{zero collapse} and the best final score on all four tasks (Chemistry,…
LLM Training moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks.
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10.48550/arXiv.2606.03532Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks.
Abstract
Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal coupling} between teacher and student -- has not been systematically studied as a stability variable. Through a controlled schedule sweep on Qwen3-8B, we establish that \emph{isolation periods}, defined as complete teacher freezing between updates, are the key structural property enabling stable learning, not teacher age. To characterize these underlying training dynamics, we introduce a diagnostic framework of temporal KL structure, refresh shock, and length-tail risk. This framework further uncovers \emph{state-oblivious collapse}: optimal short-horizon fixed schedules catastrophically fail under long-horizon training because a clock-driven refresh can copy a transiently drifting student into the teacher in a single, irreversible step. This failure mode is invisible under short-horizon evaluation and mechanistically distinct from EMA's chronic contamination. To address this, we propose \emph{Consolidation-Gated Teacher Refresh} (CGTR), which preserves isolation periods while gating each refresh on joint evidence of reward improvement and length-tail safety, ensuring every teacher movement responds to genuine student consolidation rather than a clock signal. With a single shared parameter set and no per-dataset retuning, CGTR achieves \textbf{zero collapse} and the best final score on all four tasks (Chemistry, Biology, Physics, ToolUse), self-regulating its refresh frequency to each task's learning dynamics.
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PROBLEM
Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks. Through a controlled schedule sweep on Qwen3-8B, we establish that...
METHOD
Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal coupling} between teacher and student -- has not been systematically studied as a stability variabl...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. With a single shared parameter set and no per-dataset retuning, CGTR achieves \textbf{zero collapse} and the best final score on all four tasks (Chemistry, Biology, Physics, ToolUse), self-regulating its...
WHY NOW
LLM Training moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 14, "author": "Haowei Guo; Baolong Bi; Ruicheng Zhang; Bingqian Sun; Wentao Zhang"
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Consolidation-Gated Teacher Refresh (CGTR) stabilizes self on-policy distillation by gating teacher updates on reward improvement and safety, preventing collapse and achieving state-of-the-art on multiple tasks.
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