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Restoring collaborative signals in generative recommendations can significantly boost accuracy without retraining models or sacrificing efficiency.
CostAda achieves top-tier discovery quality while using at least 50% less budget compared to existing methods, revolutionizing how we approach resource allocation in LLM search processes.
BPO achieves up to 6.1% higher success rates in sandbox-native RL tasks while cutting down on the number of required policy updates by 38%.
A unified framework reveals that the choice of tokenization and vocabulary topology can significantly influence the performance of discrete diffusion models, unlocking new avenues for optimization.
