TOPL: Training Token-Level Reward Models as Classifiers for Faithful Generation
robinomial · x · 2026-08-01
This upcoming COLM 2026 paper introduces TOPL (Token-Level Off-Policy Labeling) to address the challenge of faithful generation under distribution shift.
The core insight treats a token-level reward model as a language model. By training it for binary classification to identify whether each generated token is faithful, the model learns to spot and stop generating bad tokens. The authors highlight that this off-policy LoRA training acts as a highly interpretable form of conditional steering to improve generation quality.
Related event: USC Proposes TOPL to Enhance LLM Generation Fidelity(2 posts)→
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