arXiv Machine Learning August 11, 2026 By William Hoy, Binxu Wang, Xu Pan

Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training

Read the original on arXiv Machine Learning →

arXiv:2604. 01499v2 Announce Type: replace Abstract: Evolution Strategies (ES) have emerged as a scalable gradient-free alternative to reinforcement learning based LLM fine-tuning, but it remains unclear whether comparable task performance implies comparable solutions in parameter space.

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arXiv Machine Learning

Aug 28

Understanding Evolution Strategies for LLM Reasoning: Broader Reasoning Coverage than GRPO

The paper investigates Evolution Strategies (ES) as a memory‑efficient post‑training method for large language model (LLM) reasoning. It demonstrates that ES outperforms Group Relative Policy Optimization (GRPO) by achieving broader reasoning coverage, improving Pass@K metrics, and avoiding entropy collapse. The study also reveals that ES’s performance gains stem from sparse, high‑magnitude parameter updates, do not cause catastrophic forgetting, and can be combined with GRPO in a sequential training strategy.

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llms efficiency

arXiv Machine Learning

Jul 7

RL Forgets! Towards Continual Policy Optimization

arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.

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llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 8

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.

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arXiv Machine Learning

Jul 10

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4d ago

Learn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO

The paper introduces FastRL, a reinforcement learning framework designed to enhance the efficiency of Group Relative Policy Optimization (GRPO) and its variants. FastRL employs an advantage-aware pruning strategy that retains high-advantage trajectories while preserving gradient diversity, and an adaptive rollout sampling mechanism that adjusts sampling scale during training based on historical pruning data. Experiments show that FastRL can be integrated into GRPO, DAPO, and GSPO, yielding a 2.07× speedup on Geometry3K and GeoQA8K-R1V and a 1.64% accuracy improvement on visual reasoning benchmarks.

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arXiv Machine Learning

Jul 31

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By Yuming Yan, Kai Tang, Sihong Chen, Ke Xu, Dan Hu, Qun Yu, Pengfei Hu

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