10 citations · 13 across the 5 of their papers we have counts for
5 papers
The Majority is not always right: RL training for solution aggregation
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha +3
Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language model…
Agentic-R1: Distilled Dual-Strategy Reasoning
Weihua Du, Pranjal Aggarwal, Sean Welleck +1
Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces. Tool-augmented agents address arithmetic v…
OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
Pranjal Aggarwal, Seungone Kim, Jack Lanchantin +4
Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder…
AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement
Pranjal Aggarwal, Bryan Parno, Sean Welleck
Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal veri…
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari +5
State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful…