3 citations · 7 across the 22 of their papers we have counts for
5 papers · 1 filter
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…
When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning
Luca Viano, Antoine Moulin, Audrey Huang +3
Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications from robotics to language model training. Standard approaches…
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…
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…