5 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Past, Future, All at Once: Mitigating Stability-Plasticity Dilemma via Post-hoc JANUS Rectification
Zhilong Zheng, Letian Tao, Yang Guan +7
Fine-tuning foundation models on new tasks inevitably suffer from catastrophic forgetting. While existing works attempt to mitigate this on the basis of parameter-efficient fine-tu…
Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation
Shiqi Liu, Zeyu He, Letian Tao +9
On-policy distillation (OPD) has emerged as an effective approach for large language model post-training, yet existing objectives face a trade-off between objective fidelity and op…
FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving
Bonan Wang, Letian Tao, Bin Shuai +7
Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard parallel sampling mitigates…
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Guojian Zhan, Letian Tao, Pengcheng Wang +6
Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling com…
Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
Tianze Zhu, Yinuo Wang, Wenjun Zou +6
Reinforcement learning (RL) is a fundamental methodology in autonomous driving systems, where generative policies exhibit considerable potential by leveraging their ability to mode…
Conformal Symplectic Optimization for Stable Reinforcement Learning
Yao Lyu, Xiangteng Zhang, Shengbo Eben Li +5
Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle…