ESSAM matches PPO and GRPO accuracy (~78%) on GSM8K math tasks but uses 10-18x less GPU memory and shows stronger generalization across datasets.
Improving sharpness-aware minimization with fisher mask for better generalization on language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.
citing papers explorer
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ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
ESSAM matches PPO and GRPO accuracy (~78%) on GSM8K math tasks but uses 10-18x less GPU memory and shows stronger generalization across datasets.
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A Faster Path to Continual Learning
C-Flat Turbo accelerates continual learning by skipping redundant flatness gradients via direction-invariance observations and linear adaptive scheduling, delivering 1-1.25x speedup with comparable accuracy.