Decodable but Not Faithful: Coupling Natural-Language Rationales to Programmatic Verifiers
Language models can generate plausible rationales for their predictions, but these explanations may not faithfully represent the model's internal reasoning. We propose verifier-coupled reasoning, a framework that inserts inline claims into reasoning traces and trains an auxiliary consistency head to predict programmatic verifier outputs from rationale-span hidden states. The central finding is a gap between decodability and faithfulness: consistency training reliably makes verifier information decodable from rationale representations, but decodability does not guarantee faithful generation. In LeanCheck (formal theorem proving), rationale-only and proof-only pooling achieve perfect direct...
| Authors | Vatsal Ananthula, Adarsh Kumarappan |
|---|---|
| Primary category | cs.LG |
| Published | 19 June 2026 |
| Last updated at source | 8 August 2026 |
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