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20242026
most citedMore Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling

1 citations · 1 across the 10 of their papers we have counts for

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cs.CL2026

FinEvolveBench: A Benchmark for Self-Evolving Agents on Low-Repetition Tasks with Implicit Rewards

Zihao Deng, Yining Zhu, Leiming Wang +7

Large language model (LLM) agents increasingly rely on external experience to continually adapt to changing environments without modifying their underlying models. Recent experienc…

cs.LG2026

Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo

Weixin Wang, Yu Yang, Wei Deng +1

We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its weights. Recent Sequential Mon…

cs.LG2026

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning

Yu Yang, Yihong Guo, Anqi Liu +1

Off-dynamics offline reinforcement learning seeks to learn a target-domain policy from a large source dataset and a limited target dataset under mismatched transition dynamics. Exi…

cs.CV2026

SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents

Wencan Jiang, Jiangning Zhang, Jianbiao Mei +6

Long-horizon multimodal agents in open-world games must stay goal-directed across many low-level interactions under tight token and latency budgets. Existing approaches often trade…

cs.LG2026

Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning

Zhangjie Xia, Yu Yang, Pan Xu

Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition…