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Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills
Agamdeep Singh, Srishti Gautam, Priyanshu Gupta +3
Reasoning modes of language models outperform their non-reasoning counterparts on multi-step agentic tasks, but pay a 3-6x premium in output tokens on every episode -- much of it s…
Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge
Shashank Kirtania, Param Biyani, Priyanshu Gupta +4
Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from ex…
IndiMathBench: Autoformalizing Mathematical Reasoning Problems with a Human Touch
Param Biyani, Shashank Kirtania, Yasharth Bajpai +2
Reliable autoformalization remains challenging even in the era of large language models (LLMs). The scarcity of high-quality training data is a major bottleneck. Expert annotation…
An Empirical Investigation of Robustness in Large Language Models under Tabular Distortions
Avik Dutta, Harshit Nigam, Hosein Hasanbeig +2
We investigate how large language models (LLMs) fail when tabular data in an otherwise canonical representation is subjected to semantic and structural distortions. Our findings re…
Training Emergent Joint Associations: A Reinforcement Learning Approach to Creative Thinking in Language Models
Mukul Singh, Ananya Singha, Aishni Parab +2
Associative thinking--the ability to connect seemingly unrelated ideas--is a foundational element of human creativity and problem-solving. This paper explores whether reinforcement…
STACKFEED: Structured Textual Actor-Critic Knowledge Base Editing with FeedBack
Shashank Kirtania, Naman Gupta, Priyanshu Gupta +7
Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Aug…