CoPES: Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
Score
78提出新的协作子空间进化策略，解决资源受限下ES耗时问题，数据对比丰富。
方法描述清晰，实验设计合理，但预印本未经同行评审，细节待完善。
大幅降低显存和训练时间并有开源代码，对少卡团队极具实用价值。
当前Agent后训练成本高企，该工作切中热点，发布及时。
来自arXiv预印本，作者提供开源代码，可信度较高但未正式发表。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
Addresses a practical resource bottleneck with solid experiments and open-source code, yet still a preprint awaiting peer review.
AI commentary
「What strikes me most is how it fuses ES's memory efficiency with coevolution to attack the 'few GPUs' bottleneck. Recovering 92% of GRPO's gain is impressive, and open-sourcing the code makes it immediately actionable. For small teams doing LLM alignment or agent training, this might be a more compelling alternative to LoRA.」
Quick overview
Tool-using LLM agents generate long multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution strategies (ES) enable memory-efficient full-parameter training without backpropagation but demand high GPU-hours. To address this, the authors introduce CoPES, a cooperative coevolutionary method that decomposes the full parameter space into low-dimensional subspaces and searches cooperatively. Post-training Qwen3.5-4B for math tool-use, under the same GPU-hour budget as full-parameter GRPO's best checkpoint, CoPES recovers 92% of GRPO's validation-accuracy gain versus 67% for standard ES, while requiring less than one-eighth of the GPU memory. It outperforms standard ES and LoRA-based GRPO across all pass@k metrics on five benchmarks. Code is open-sourced.
- CoPES decomposes parameter space into low-dimensional subspaces and searches cooperatively, drastically reducing training time for ES under limited GPUs.
- On a Qwen3.5-4B math agent, it recovers 92% of GRPO's gain under the same GPU budget, with memory below 1/8 of full-parameter GRPO.
- It outperforms standard ES and LoRA-based GRPO on pass@k across five benchmarks, and also shows gains on QA.
- Code is open-sourced on GitHub.
Full text
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