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AIM-ZO is proposed, a ZO fine-tuning method based on Activation-Informed Subspace Maintenance that uses forward activations as local directional information and continuously integrates them into a broad, evolving subspace over training.
In-cell learning is introduced, a paradigm for writing new knowledge only within these cells, so that re-quantizing the served weights reproduces the released integer codes and scales exactly.
These findings position ES as a distinct reasoning post-training paradigm rather than a less effective, memory-efficient alternative to GRPO, and study how hyperparameter design affects the effectiveness of ES, demonstrating that ES requires a smaller population size in a larger LLM.
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