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We study learning rate (LR) scaling for hybrid architectures combining Transformer and State-Space Model (SSM) blocks, a class adopted by several recent production language models. In particular, we focus on the gap between the theoretical scaling rules derived for SSMs under zero-order-hold (ZOH) discretization at inf...
This work formalizes a training rule consistent with both Masked Boundary Pause (MBP) and Masked Boundary Pause (MBP), pause tokens placed at reasoning-step boundaries with their loss masked, and demonstrates that this mode-preserving strategy extend gains to GRPO.
DCGC is introduced, a Masked Diffusion Model (MDM) framework for global correction that uses an imperfect solution draft from an upstream solver as auxiliary context that improves full test set accuracy by correcting low-consensus upstream outputs, highlighting its utility as a verifier-free global correction module fo...
This work proposes a circuit-grounded framework that connects training-dynamics-based data valuation with mechanistic interpretability (MI), and conceptualizes data quality along three complementary utility axes, learnability, challenge, and alignment, establishing a principled white-box paradigm for interpretable data...
STEP-KTODER is proposed, a framework for code preference optimization that defines steps as module-level functions in decomposed multi-function programs and assigns binary correctness labels via automatically generated unit tests and shows that execution-based labels are essential.
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