5 citations · 11 across the 10 of their papers we have counts for
7 papers · 1 filter
Predicting Privileged Information Effectiveness for On-Policy Self-Distillation
Kaijian Zou, Emiliano Penaloza, MohammadHossein Rezaei +5
On-policy self-distillation (OPSD) enables large language models (LLMs) to learn from privileged information (PI) without a separate teacher model. However, prior work largely trea…
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal +1
We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically…
Agentic Rubrics as Contextual Verifiers for SWE Agents
Mohit Raghavendra, Anisha Gunjal, Bing Liu +1
Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite it…
TutorBench: A Benchmark To Assess Tutoring Capabilities Of Large Language Models
Rakshith S Srinivasa, Zora Che, Chen Bo Calvin Zhang +11
As students increasingly adopt large language models (LLMs) as learning aids, it is crucial to build models that are adept at handling the nuances of tutoring: they need to identif…
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…
Task-Induced Representation Learning
Jun Yamada, Karl Pertsch, Anisha Gunjal +1
In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for eff…