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Standard GRPO's uniform clipping boundary actively chokes exploration by penalizing rare breakthroughs on hard problems just as harshly as trivial rollouts on easy ones.
GenRec shows that LLM-backed recommenders can outperform traditional models with significantly less training data, revolutionizing the recommendation landscape.
