EMES is the first work to incorporate EM into controllable DMs and to leverage EM for guiding data synthesis in RL, and introduces a hashing-based state representation for EM to improve its efficiency and further boost the quality of synthetic data.
Experimental results demonstrate that the proposed uncertainty-aware replay buffer enables an RL agent to obtain higher rewards during training compared to other existing uncertainty-aware RL frameworks.
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis J. Desell et al.· 0 citations
Novelty and Surprise Prioritized Experience Replay (NSPER) is introduced, which uses novelty to capture underrepresented states and surprise to expose gaps in the agent's understanding of the environment and is extended with NSPER+R, integrating these signals as intrinsic rewards to jointly improve replay quality and e...
Hoda Yamani, Henry Williams, Bruce A. MacDonald· 0 citations
ESRL is introduced, an architecture-aware framework that explicitly explores the expert-routing space of MoE models, and preserves high-confidence experts as anchors, and restricts stochastic routing to a plausible candidate pool, thereby retaining reliable computation paths.
Hong-Yi He, Zheng-Wen Lin, Xiao Liu et al.· 0 citations
River, a simple training recipe that improves reward quality by filtering low-quality environments and augmenting outcome rewards with process-level behavior regularization is proposed, which achieves the best performance among evaluated open-source RL-trained 8B models across four terminal-agent benchmarks.
Yi-Fan Yao, Bo Pang, Xuan-Phi Nguyen et al.· 2 citations
This work proposes neurosymbolic HRL with {\em Incremental Knowledge (InK), where symbolic high-level components perform symbolic planning on an updatable representation of current InK, while low-level goal-conditioned neural modules learn motion primitives through experience using reward shaping.
Subrat Prasad Panda, B. Genest, A. Easwaran· Lecture notes in computer sc...· 0 citations
This work proposes EDGE (Experience-Distillation for Guided Exploration), a framework that treats retrieved experiences as temporary training-time scaffolds and progressively internalizes their benefits into the parametric policy.
Can Xie, Yu-Yi Zhou, Wen Yang et al.· 2 citations
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