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OPERA proposes a reinforcement learning method for open-ended tasks using intrinsic rewards based on perplexity dynamics, replacing unreliable LLM-as-a-judge reward models. It achieves state-of-the-art results on Qwen3-8B, matching proprietary models in creative writing and other open-ended tasks.