1 citations · 2 across the 8 of their papers we have counts for
8 papers
From Offline Proxies to Online Decisions: A Layered Engagement Evaluation Framework for Conversational AI
Xuanyi Li, Vaskar Nath, Hossein Amirkhani +2
Online A/B experiments are the decision standard for user engagement, but traffic and readout time limit how many conversational-AI changes can be tested. We ask whether an offline…
Progress over Points: Reframing LM Benchmarks Around Scientific Objectives
Alwin Jin, Sean M. Hendryx, Vaskar Nath
Current benchmarks that test LLMs on static, already-solved problems (e.g., math word problems) effectively demonstrated basic capability acquisition. The natural progression has b…
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
Anisha Gunjal, Anthony Wang, Elaine Lau +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for complex reasoning tasks with clear correctness signals such as math and coding. However, extending it…
Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models
Vaskar Nath, Elaine Lau, Anisha Gunjal +3
We study the process through which reasoning models trained with reinforcement learning on verifiable rewards (RLVR) can learn to solve new problems. We find that RLVR drives perfo…
ToolComp: A Multi-Tool Reasoning & Process Supervision Benchmark
Vaskar Nath, Pranav Raja, Claire Yoon +1
Despite recent advances in AI, the development of systems capable of executing complex, multi-step reasoning tasks involving multiple tools remains a significant challenge. Current…
Planning In Natural Language Improves LLM Search For Code Generation
Evan Wang, Federico Cassano, Catherine Wu +7
While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize tha…
Revisiting the Superficial Alignment Hypothesis
Mohit Raghavendra, Vaskar Nath, Sean Hendryx
The Superficial Alignment Hypothesis posits that almost all of a language model's abilities and knowledge are learned during pre-training, while post-training is about giving a mod…
Learning Goal-Conditioned Representations for Language Reward Models
Vaskar Nath, Dylan Slack, Jeff Da +4
Techniques that learn improved representations via offline data or self-supervised objectives have shown impressive results in traditional reinforcement learning (RL). Nevertheless…