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Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

90 upvotes · 18 AUG 2026 · Zhi Zheng, Rongsheng Chen, Yunpeng Ba et al.

This paper proposes a new method for fine-tuning large language models (LLMs) in reinforcement learning (RL) tasks with long horizons, using evolution strategies (ES) instead of traditional backpropagation-based training. Practitioners might care because it allows for more efficient and flexible fine-tuning of LLMs, enabling them to tackle complex tasks with larger models and longer interactions.

evolution strategiesreinforcement learninglong-horizon optimizationlanguage modelscontinual learning