11 citations · 44 across the 10 of their papers we have counts for
10 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…
Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1
Karthik Valmeekam, Kaya Stechly, Atharva Gundawar +1
The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI…
LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati
The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI…
Chain of Thoughtlessness? An Analysis of CoT in Planning
Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
Large language model (LLM) performance on reasoning problems typically does not generalize out of distribution. Previous work has claimed that this can be mitigated with chain of t…
On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks
Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati
There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically w…
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan +5
There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these…
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically w…