34 citations · 39 across the 6 of their papers we have counts for
6 papers
Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity
Vardhan Palod, Karthik Valmeekam, Kaya Stechly +1
Intermediate token generation (ITG), where a model produces output before the solution, has been proposed as a method to improve the performance of language models on reasoning tas…
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…
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…
Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach
Atharva Gundawar, Karthik Valmeekam, Mudit Verma +1
Previous work has attempted to boost Large Language Model (LLM) performance on planning and scheduling tasks through a variety of prompt engineering techniques. While these methods…
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
Lin Guan, Karthik Valmeekam, Sarath Sreedharan +1
There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical…