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Fudan University
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Naive RL in recommender systems suffers from biased gradients that favor longer paths, but ProRL fixes this with a novel reward centering and advantage estimation scheme.
LLM agent performance hinges on maximizing decision-relevant information density within context, not just context length, and GenericAgent proves it.
