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> Large language models are usually trained, distributed, and consumed as monolithic systems even though their internal computations are structurally modular. This paper asks a stronger question than model splitting, routing, merging, or adapter composition: can internal language-model subsystems become independently p...
Reinforcement learning with verifiable rewards (RLVR) evaluates decoded outcomes, whereas policy-gradient estimators operate on concrete trajectories and batch-dependent training state. These two levels need not respect the same equivalence relation. We formalize estimator homomorphism compatibility: when several concr...
Reinforcement learning with verifiable rewards (RLVR) evaluates decoded outcomes, whereas policy-gradient estimators operate on concrete trajectories and batch-dependent training state. These two levels need not respect the same equivalence relation. We formalize estimator homomorphism compatibility: when several concr...
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