ReMaRk improves reinforcement learning for recommenders by training on the union of all valid replies in a thread rather than selecting a single response. This approach achieves the same validation recall as Rank-GRPO while using only 25% to 53% of the training steps.
HOW THIS AFFECTS YOU
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builderYou can train more efficient and cost-effective recommendation models using this RL recipe.
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researcherThis method addresses the high variance caused by sampling single replies in noisy training data.