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20162026
most citedDistributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies

593 citations · 1.8k across the 57 of their papers we have counts for

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Showing 2026 · cs.LGShow all

6 papers · 2 filters

cs.LG2026

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…

cs.LG2026

Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization

Zhixin Ren, Yao Lyu, Congrong Li +2

Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood. W…

cs.LG2026

On the Identifiability of Controlled World Models

Xiangteng Zhang, Yang Guan, Bo Zhang +3

World model serves as a promising tool to infer environment dynamics under high-dimensional observations and candidate actions. Recently, LeCun's JEPA provides a compelling framewo…

cs.LG2026

Distributional Soft Bellman Operator under the Cramér Geometry

Keru Wang, Yixin Deng, Yao Lyu +2

Distributional soft policy iteration (DSPI) provides an important framework for combining distributional reinforcement learning (DRL) with maximum-entropy control, in which the pol…

cs.LG2026

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…

cs.LG2026

On the Equilibrium between Feasible Zone and Uncertain Model in Safe Exploration

Yujie Yang, Zhilong Zheng, Shengbo Eben Li

Ensuring the safety of environmental exploration is a critical problem in reinforcement learning (RL). While limiting exploration to a feasible zone has become widely accepted as a…