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Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computation. Synthetic environments reduce these costs while introducing variations in data and experimental settings that require task-specific diagnosis. Access to diagnostic to...
Fed-LSVI is proposed, the first provably efficient federated algorithm for online reinforcement learning with linear function approximation in episodic Markov decision processes and achieves a regret bound of $\widetilde{\mathcal O}(\sqrt{Md^3H^4T})$, matching the best-known regret for multi-agent online reinforcement...
Difficulty-Aware Semantic-ID Optimization (DASO), a tree-aware post-training method that addresses failure mode as an online rollout-allocation problem and improves over MiniOneRec-style GRPO on 11 of 12 metrics and achieves the best result on 9 of 12 metrics.
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