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Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path: privileged information extracted from experience is provided to a teacher, whose behavior is then distilled into a student model that has no access...
The results support a compact, Fisher-conditioned privileged subspace for short-budget OPSD, with strict gains in 11 of the 12 model-checkpoint combinations and an exact tie at 4B step 25.
Unified On-Policy Self-Distillation (USD), a lightweight online algorithm to solve the Lagrangian, reveals that a single dual variable governs both decisions: at one price for learning difficulty, it simultaneously sets the token-selection threshold and the direction of privileged-information adjustment, keeping superv...
This work evaluates SymBuild in three construction domains: computer-aided design (CAD) assembly, Mini-Programs, and exact-fill packing, and test additional framework instantiations in all four domains, demonstrating that SymBuild is an effective, analyzable method for anytime verified construction.
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