4 citations · 4 across the 3 of their papers we have counts for
6 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…
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…
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…