3 citations · 3 across the 8 of their papers we have counts for
8 papers
Policy-Guided Stepwise Model Routing for Cost-Effective Reasoning
Wenwen Si, Insup Lee, Osbert Bastani
Inference-time computation has greatly enhanced the performance of large language models (LLMs) on challenging reasoning tasks, but this strategy can incur high inference costs. On…
RAPID: An Efficient Reinforcement Learning Algorithm for Small Language Models
Lianghuan Huang, Sagnik Anupam, Insup Lee +2
Reinforcement learning (RL) has emerged as a promising strategy for finetuning small language models (SLMs) to solve targeted tasks such as math and coding. However, RL algorithms…
RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out…
Effective Reinforcement Learning for Reasoning in Language Models
Lianghuan Huang, Shuo Li, Sagnik Anupam +2
Reinforcement learning (RL) has emerged as a promising strategy for improving the reasoning capabilities of language models (LMs) in domains such as mathematics and coding. However…
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the…
Uncertainty in Language Models: Assessment through Rank-Calibration
Xinmeng Huang, Shuo Li, Mengxin Yu +5
Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctl…
PAC Prediction Sets Under Label Shift
Wenwen Si, Sangdon Park, Insup Lee +2
Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes.…
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Ramneet Kaur, Kaustubh Sridhar, Sangdon Park +4
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applicatio…