Time series forecasting (TSF) plays an important role in a wide range of real-world applications. Recently, time series foundation models (TSFMs), pretrained on large-scale datasets, have demonstrated strong generalization capabilities and emerged as an important paradigm for TSF. Reinforcement learning (RL) post-train...
Jian-Qi Zhang, Xing-Yu Zhang, Ze-En Song et al.· 1 citation
Pre-trained 3D vision models have substantially advanced point cloud analysis, yet adapting them to downstream tasks via full fine-tuning is computationally expensive and storage-intensive. Parameter-Efficient Fine-Tuning (PEFT) offers a promising alternative by reducing both adaptation cost and storage burden. However...
Zi-Xiang Ai, Zhen-Yu Cui, Yufei Guo et al.· IEEE Transactions on Pattern...· 0 citations
Temporal GRPO addresses the problem of trajectory-level credit aliasing in post-train VLA policies by constructing detectable task stages, aligning each rollout with stage-specific action intervals, and comparing only rollouts that have entered the same stage.
Gradient Uncertainty-Aware Policy Optimization is proposed, which models each group gradient as a random variable under a Bayesian formulation and estimates its probability distribution and derives gradient uncertainty using a Dirichlet-based formulation and uses it to calibrate the contribution of each group gradient...
Peizheng Guo, Jian-Qi Zhang, Xing-Yu Zhang et al.· 0 citations
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