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About this paper
Vishak K Bhat, Prateek Chanda, Vijval Ekbote, Ashmit Khandelwal, Maitreyi Swaroop, Vineeth N. Balasubramanian, Subbarao Kambhampati, Nagarajan Natarajan and 1 more
The `interwhen` framework enhances the reliability and efficiency of LLM-based reasoning by steering their intermediate steps with test-time verification. It leverages asynchronously executed, policy-derived verifiers to provide real-time feedback, enabling LLMs to self-correct during generation and achieving significant improvements in task completion and policy compliance, such as a 55.53 percentage point gain on the τ²-bench Telecom domain, often with reduced token usage.
Key insights
- Decoupling verifier execution from the main generation process and running verifiers asynchronously enables efficient process-level oversight with minimal latency overhead for compliant executions.
- Automated synthesis of provably sound verifiers from natural language policies significantly scales the creation of reliable verification logic, reducing manual effort for policy adaptation.
- Direct, real-time feedback on intermediate reasoning steps effectively steers LLMs toward policy compliance and more efficient problem-solving, often leading to reduced token usage compared to unguided generation.
Summary by alphaXiv.
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