DAIS: Dependency-Aware Intermediate QA Supervision Boosts Complex Reasoning
Researchers introduce Dependency-Aware Intermediate QA Supervision (DAIS), a training framework that transforms teacher rationales into stage-level QA records, each conditioned on relevant previous states. DAIS improves final-answer accuracy across multiple datasets and model backbones, achieving up to a 5.6% accuracy gain over the strongest baseline on policy-compliance benchmarks. Controlled ablations indicate that dependency-conditioned supervision provides benefits beyond simply adding intermediate text.
Why it matters: DAIS demonstrates a lightweight method for enhancing complex reasoning in language models without altering inference procedures.
Full story at: arXiv Computation and Language ↗