Modal's Deep Dive: Post-hoc Corrections Are Beating Exact Matching in RL Infrastructure
charles_irl · x · 2026-10-14
Modal engineer Nan Jiang published what David Bellamy calls the best synthesis of the past year's RL infrastructure ideas. Its core thesis: post-hoc corrections for trainer-inference mismatch (TITO, top-p mask replay) are winning out over exact-matching approaches like R3, deterministic kernels, precision matching, near-zero staleness, and even accelerator matching. Bellamy adds open questions on weight deltas for multi-cluster heterogeneous-chip RL.
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