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LLMs can learn effective dynamic embeddings for massive combinatorial optimization problems with a shallow policy by selectively propagating memory across rollout steps, bypassing the need for expensive full recomputation or external labels.
ES not only outperforms GRPO in reasoning tasks but also reveals that substantial parameter updates don't equate to widespread functional changes in LLMs.
Evolution strategies can optimize large language models for long-horizon tasks with minimal GPU resources, outperforming traditional reinforcement learning approaches.
