Qian Lin

[Daniel Brown @daniel_s_brown]

Our paper "A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons" is being presented this week at ICRA 2026!

https://t.co/XaJV2TJSbZ

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[Qian Lin @QianLin44]

Across safe RL environments and a proof-of-concept LLM-style setting, our method reduces safety cost without explicit safety rewards, matches oracle safety baselines in task performance, and remains robust to preference imbalance.

🧵7/7

[Qian Lin @QianLin44]

What if your task reward is imperfect — especially when it misses safety-related objectives? Maybe crowd preferences can help.

Excited to share our paper, Implicit Safety Alignment from Crowd Preferences, accepted to ICML 2026! Grateful to @daniel_s_brown for his guidance and support on this work.

Paper: https://t.co/UbBhas54Kw

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[Qian Lin @QianLin44]

Our approach: compose safe skills instead of combining safety rewards. We first learn preference-aligned skills that capture different user preferences from crowd data. Then, a high-level policy composes these skills to solve new downstream tasks. The key idea is that the high-level policy solves the task, while the low-level skills provide safety

🧵6/7

[Qian Lin @QianLin44]

Across safe RL environments and a proof-of-concept LLM-style setting, our method reduces safety cost without explicit safety rewards, matches oracle safety baselines in task performance, and remains robust to preference imbalance.

🧵7/7

[Qian Lin @QianLin44]

Our approach: compose safe skills instead of combining safety rewards. We first learn preference-aligned skills that capture different user preferences from crowd data. Then, a high-level policy composes these skills to solve new downstream tasks. The key idea is that the high-level policy solves the task, while the low-level skills provide safety

🧵6/7

[Zifan Wu @zifan_w]

Excited to share that our paper, "Understanding the Effects of Neuron Dominance in Deep Reinforcement Learning", has been published in Transactions on Machine Learning Research!
Full paper: https://t.co/M6U7X3bKjC
Work with Qian Lin, Blake Lawlor, Haijun Zhao, and Daniel Brown.

[Daniel Brown @daniel_s_brown]

Super excited about our lab's new paper led by @zifan_w: "Understanding the Effects of Neuron Dominance in Deep Reinforcement Learning", that has been published in Transactions on Machine Learning Research (TMLR)!
https://t.co/Tm76XFMSmm

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