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Envs-FORGE is presented, a prompting policy that converts verifier rewards into per-seed environment-synthesis actions and solves a per-seed mixed-integer linear program (MILP) to choose the action that conditions generation.
Direct On-Policy Distillation (Direct-OPD) is proposed, which transfers the teacher's RL-induced policy shift instead of running sparse-reward RL on the target model and consistently leverages weaker teachers to improve stronger target models.
Subspace-Aligned Rewiring (SAR) shows that extracting reasoning-effective updates from parameter geometry can serve as a training-free mechanism to improve reasoning and multi-domain performance.
Open-MOPD, a principled framework incorporating token-share balancing, gap-aware dynamic budget allocation, and student reward refresh, systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student.
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