5 citations · 11 across the 10 of their papers we have counts for
13 papers
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…
Structural properties of admissible subgraphs and the depth of symbolic powers of cover ideals
Nguyen Thu Hang, Nguyen Thi Thanh Tam, Thanh Vu
Let be a simple graph with cover ideal in a polynomial ring $S = \k[x_1, \ldots, x_n]$. For each integer , let denote the maximum size of an ordered ma…
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…
Reward Hacking in Rubric-Based Reinforcement Learning
Anas Mahmoud, MohammadHossein Rezaei, Zihao Wang +3
Reinforcement learning with verifiable rewards has enabled strong post-training gains in domains such as math and coding, though many open-ended settings rely on rubric-based rewar…
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…
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning
Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang +21
Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to…
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…
Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification
Anisha Gunjal, Greg Durrett
Automatic factuality verification of large language model (LLM) generations is becoming more and more widely used to combat hallucinations. A major point of tension in the literatu…
Detecting and Preventing Hallucinations in Large Vision Language Models
Anisha Gunjal, Jihan Yin, Erhan Bas
Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in generalizing across a diverse set of multi-modal tasks, especially for Visual Question Answeri…
Drafting Event Schemas using Language Models
Anisha Gunjal, Greg Durrett
Past work has studied event prediction and event language modeling, sometimes mediated through structured representations of knowledge in the form of event schemas. Such schemas ca…
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…