3 citations · 6 across the 19 of their papers we have counts for
8 papers · 1 filter
Fork-and-Flush: Escaping Idea Basins in Autoresearch Agents
Ziyang Cai, Christos Ziakas, Vasilis Kontonis +5
Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent experiments. We show that indepe…
Scaling Discovery through Test-Time Communication
Jongho Park, Vasilis Kontonis, Shivam Garg +2
Science advances not in isolation but through collaboration, yet existing agentic systems capture little of this. Whether communicating agents help remains an open question with mi…
TailSFT: Filtered Fine-Tuning Improves Post-Training Performance
Sadhika Malladi, Samy Jelassi, Dylan Foster +2
Reinforcement learning post-training drives reasoning and agentic capabilities in modern AI systems, yet a growing body of work shows that it is most effective when used to fine-tu…
Learning to Reason with Curriculum II: Compositional Generalization
Nived Rajaraman, Audrey Huang, Miroslav Dudik +3
Compositional generalization, the ability to solve complex problems by combining solutions to simpler sub-problems, is a fundamental capability of both natural and artificial intel…
Select and Improve: Understanding the Mechanics of Post-Training for Reasoning
Akshay Krishnamurthy, Audrey Huang, Nived Rajaraman
Reinforcement learning has rapidly emerged as a key component in the training of reasoning and coding models, yet it remains poorly understood from a mechanistic perspective. We st…
Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
Noah Golowich, Fan Chen, Dhruv Rohatgi +4
Inference-time methods that aggregate and prune multiple samples have emerged as a powerful paradigm for steering large language models, yet we lack any principled understanding of…
Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum
Nived Rajaraman, Audrey Huang, Miro Dudik +3
Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven significant advances in model capa…
A Unifying View of Coverage in Linear Off-Policy Evaluation
Philip Amortila, Audrey Huang, Akshay Krishnamurthy +1
Off-policy evaluation (OPE) is a fundamental task in reinforcement learning (RL). In the classic setting of linear OPE, finite-sample guarantees often take the form $$ \textrm{Eval…