4 citations · 8 across the 6 of their papers we have counts for
6 papers
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
Context Steering: Controllable Personalization at Inference Time
Jerry Zhi-Yang He, Sashrika Pandey, Mariah L. Schrum +1
To deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate context -- personal, demographic, and cultural information specific to an…
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown +1
Our ultimate goal is to build robust policies for robots that assist people. What makes this hard is that people can behave unexpectedly at test time, potentially interacting with…
Learning Representations that Enable Generalization in Assistive Tasks
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown +2
Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ''population'' of environments (i.e. domain randomi…
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson +2
Learning policies via preference-based reward learning is an increasingly popular method for customizing agent behavior, but has been shown anecdotally to be prone to spurious corr…
Assisted Robust Reward Design
Jerry Zhi-Yang He, Anca D. Dragan
Real-world robotic tasks require complex reward functions. When we define the problem the robot needs to solve, we pretend that a designer specifies this complex reward exactly, an…