15 papers
Masked Distillation: Internalizing the Chain-of-Thought in Language Models
Durgesh Kalwar, Vardhan Palod, Subbarao Kambhampati
Large Reasoning Models (LRMs) produce long, explicit chains of intermediate steps before generating a final answer at inference time. These intermediate traces dominate latency, me…
Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
Subbarao Kambhampati, Karthik Valmeekam, Siddhant Bhambri +6
Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning task…
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
Upasana Biswas, Durgesh Kalwar, Subbarao Kambhampati +1
Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior. Existing approaches attempt to appr…
Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
Karthik Valmeekam, Vardhan Palod, Kaya Stechly +2
Recent impressive results from large reasoning models have been interpreted as a triumph of Chain of Thought (CoT), and especially of the process of training on CoTs sampled from b…
Evaluating the False Trust Engendered by LLM Explanations
Vardhan Palod, Upasana Biswas, Subbarao Kambhampati
Large Language Models (LLMs) and Large Reasoning Models (LRMs) are increasingly used for critical tasks, yet they provide no guarantees about the correctness of their solutions. Us…
interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification
Vishak K Bhat, Prateek Chanda, Vijval Ekbote +6
Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctness. Existing approaches either verify o…