5 citations · 5 across the 9 of their papers we have counts for
10 papers
Correct Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces
Ratish Puduppully, Pranabendu Misra, Paarth Iyer +3
Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interpretation i…
Masked Self-Distillation: Internalizing the Chain-of-Thought in Language Models
Durgesh Kalwar, Vardhan Palod, Subbarao Kambhampati +1
Large Reasoning Models produce long, explicit chains of intermediate steps before generating a final answer at inference time. These intermediate traces dominate latency, memory us…
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…
Local Coherence or Global Validity? Investigating RLVR Traces in Math Domains
Soumya Rani Samineni, Durgesh Kalwar, Vardaan Gangal +2
Reinforcement Learning with Verifiable Rewards (RLVR)-based post-training of Large Language Models (LLMs) has been shown to improve accuracy on reasoning tasks and continues to att…
Efficiency Boost in Decentralized Optimization: Reimagining Neighborhood Aggregation with Minimal Overhead
Durgesh Kalwar, Mayank Baranwal, Harshad Khadilkar
In today's data-sensitive landscape, distributed learning emerges as a vital tool, not only fortifying privacy measures but also streamlining computational operations. This becomes…
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…
Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Durgesh Kalwar, Omkar Shelke, Harshad Khadilkar
We consider the inventory management problem, where the goal is to balance conflicting objectives such as availability and wastage of a large range of products in a store. We propo…
Safe Sequential Optimization for Switching Environments
Durgesh Kalwar, Vineeth B. S
We consider the problem of designing a sequential decision making agent to maximize an unknown time-varying function which switches with time. At each step, the agent receives an o…
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Durgesh Kalwar, Omkar Shelke, Somjit Nath +2
Improving sample efficiency is a key challenge in reinforcement learning, especially in environments with large state spaces and sparse rewards. In literature, this is resolved eit…