5 papers
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
Ashima Suvarna, Kendrick Phan, Mehrab Beikzadeh +2
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved reasoning in formal domains such as mathematics and code, but extending these gains beyond STEM rem…
When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
Nishad Singhi, Hritik Bansal, Arian Hosseini +4
Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-so…
BIG-Bench Extra Hard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal +17
Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LL…
Generative Verifiers: Reward Modeling as Next-Token Prediction
Lunjun Zhang, Arian Hosseini, Hritik Bansal +3
Verifiers or reward models are often used to enhance the reasoning performance of large language models (LLMs). A common approach is the Best-of-N method, where N candidate solutio…
Peeking Behind Closed Doors: Risks of LLM Evaluation by Private Data Curators
Hritik Bansal, Pratyush Maini
The rapid advancement in building large language models (LLMs) has intensified competition among big-tech companies and AI startups. In this regard, model evaluations are critical…