5 papers
Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Zhiting Mei, Christina Zhang, Tenny Yin +3
Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced using reinforcement learning. However, li…
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
Ola Shorinwa, Zhiting Mei, Justin Lidard +2
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society.…
Guiding Data Collection via Factored Scaling Curves
Lihan Zha, Apurva Badithela, Michael Zhang +7
Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different condition…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…
Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction
Justin Lidard, Hang Pham, Ariel Bachman +2
Tasks where robots must anticipate human intent, such as navigating around a cluttered home or sorting everyday items, are challenging because they exhibit a wide range of valid ac…