Bo Yang

Tianci Gao, Konstantin A. Neusypin, Dmitry D. Dmitriev et al.

Proximal Policy Optimization (PPO) is widely used in continuous control due to its robustness and stable training, yet it remains sample-inefficient in tasks with expensive interactions and high-dimensional action spaces. This paper proposes PPO-DAP (PPO with Diffusion Action Prior), a strictly on-policy framework that improves exploration quality and learning efficiency without modifying the PPO objective. PPO-DAP follows a two-stage protocol. Offline, we pretrain a conditional diffusion action prior on logged trajectories to cover the action distribution supported by the behavior policy. Online, PPO updates the actor-critic only using newly collected on-policy rollouts, while the diffusion prior is adapted around the on-policy state distribution via parameter-efficient tuning (Adapter/LoRA) over a small parameter subset. For each on-policy state, the prior generates multiple action proposals and concentrates them toward high-value regions using critic-based energy reweighting and in-denoising gradient guidance. These proposals affect the actor only through a low-weight imitation loss and an optional soft KL regularizer to the prior; importantly, PPO gradients are never backpropagated through offline logs or purely synthetic trajectories. We further analyze the method from a dual-proximal perspective and derive a one-step performance lower bound. Across eight MuJoCo continuous-control tasks under a unified online budget of 1.0M environment steps, PPO-DAP consistently improves early learning efficiency (area under the learning curve over the first 40 epochs, ALC@40) and matches or exceeds the strongest on-policy baselines in final return on 6/8 tasks, with modest overhead (1.18+/-0.04x wall-clock time and 1.05+/-0.02x peak GPU memory relative to PPO).

Dongsheng Li, Haodong Liu, Chao Chen et al.

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this paper, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.