20 citations · 22 across the 7 of their papers we have counts for
9 papers
Do Thinking Tokens Help with Safety?
Narutatsu Ri, Abhishek Panigrahi, Sanjeev Arora
Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts. It is also generally believed that this more "d…
Skill-Targeted Adaptive Training
Yinghui He, Abhishek Panigrahi, Yong Lin +1
Language models often show little to no improvement (i.e., "saturation") when trained via vanilla supervised fine-tuning (SFT) on data similar to what they saw in their training se…
Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction
Yong Lin, Shange Tang, Bohan Lyu +17
We introduce Goedel-Prover-V2, a series of open-source language models that set a new state-of-the-art in automated theorem proving. Built on the standard expert iteration and rein…
Advancing Science- and Evidence-based AI Policy
Rishi Bommasani, Sanjeev Arora, Jennifer Chayes +17
AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium o…
Why is Your Language Model a Poor Implicit Reward Model?
Noam Razin, Yong Lin, Jiarui Yao +1
Reward models are key to language model post-training and inference pipelines. Conveniently, recent work showed that every language model defines an implicit reward model (IM-RM),…
LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Zihan Zheng, Zerui Cheng, Zeyu Shen +16
Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algor…
Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities
Haoyu Zhao, Yihan Geng, Shange Tang +5
LLM-based formal proof assistants (e.g., in Lean) hold great promise for automating mathematical discovery. But beyond syntactic correctness, do these systems truly understand math…
AdaptMI: Adaptive Skill-based In-context Math Instruction for Small Language Models
Yinghui He, Abhishek Panigrahi, Yong Lin +1
In-context learning (ICL) allows a language model to improve its problem-solving capability when provided with suitable information in context. Since the choice of in-context infor…
Extending and Improving Wordnet via Unsupervised Word Embeddings
Mikhail Khodak, Andrej Risteski, Christiane Fellbaum +1
This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our m…