76 citations · 390 across the 60 of their papers we have counts for
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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…
RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs
Soumya Rani Samineni, Durgesh Kalwar, Karthik Valmeekam +2
Reinforcement learning based post-training of large language models (LLMs) has recently gained attention, particularly following the release of DeepSeek R1, which applied GRPO for…
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning
Siddhant Bhambri, Amrita Bhattacharjee, Durgesh Kalwar +3
Reinforcement Learning (RL) suffers from sample inefficiency in sparse reward domains, and the problem is further pronounced in case of stochastic transitions. To improve the sampl…
Data Driven Reward Initialization for Preference based Reinforcement Learning
Mudit Verma, Subbarao Kambhampati
Preference-based Reinforcement Learning (PbRL) methods utilize binary feedback from the human in the loop (HiL) over queried trajectory pairs to learn a reward model in an attempt…
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Yantian Zha, Lin Guan, Subbarao Kambhampati
Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in m…