Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution strategies (ES) enable memory-efficient full-parameter post-training without backpropagation and can eventually match the performance of gradient-based reinforcement learning (RL). However, resource-constrained settings typically offer only a few GPUs, so the high GPU-hour requirements of ES translate into prohibitively long training times. To addres

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ethics.ai (3 August 2026), “Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training,” evidence record 16100, https://ethics.ai/record/16100 (originally published by arXiv cs.AI).

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