10 citations · 33 across the 14 of their papers we have counts for
14 papers
Verus-SpecGym: An Agentic Environment for Evaluating Specification Autoformalization
Anmol Agarwal, Natalie Neamtu, Pranjal Aggarwal +6
AI coding agents are increasingly used to write real-world software, but ensuring that their outputs are correct remains a fundamental challenge. Formal verification offers a promi…
Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim +18
The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must cul…
Propose, Solve, Verify: Self-Play Through Formal Verification
Alex Wilf, Pranjal Aggarwal, Bryan Parno +4
Training models through self-play alone (without any human data) has been a longstanding goal in AI, but its effectiveness for training large language models remains unclear, parti…
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…
L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
Pranjal Aggarwal, Sean Welleck
Reasoning language models have shown an uncanny ability to improve performance at test-time by ``thinking longer''-that is, by generating longer chain-of-thought sequences and henc…
Programming with Pixels: Can Computer-Use Agents do Software Engineering?
Pranjal Aggarwal, Sean Welleck
Computer-use agents (CUAs) hold the promise of performing a wide variety of general tasks, but current evaluations have primarily focused on simple scenarios. It therefore remains…
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…
GEO: Generative Engine Optimization
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit +3
The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. Th…
AutoMix: Automatically Mixing Language Models
Pranjal Aggarwal, Aman Madaan, Ankit Anand +10
Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively le…
Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
Pranjal Aggarwal, Aman Madaan, Yiming Yang +1
A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution…
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
Pranjal Aggarwal, Ameet Deshpande, Karthik Narasimhan
Extreme classification (XC) involves predicting over large numbers of classes (thousands to millions), with real-world applications like news article classification and e-commerce…