PostTrainBench: Can LLM Agents Automate LLM Post-Training?
AI agents have become surprisingly proficient at software engineering over the past year, largely due to improvements in reasoning capabilities. This raises a deeper question: can these systems extend their capabilities to automate AI research itself? In this paper, we explore post-training, the critical phase that turns base LLMs into useful assistants. We introduce PostTrainBench to benchmark how well LLM agents can perform post-training autonomously under bounded compute constraints (10 hours on one H100 GPU). We ask frontier agents (e.g., Claude Code with Opus 4.6) to optimize the performance of a base LLM on a particular benchmark (e.g., Qwen3-4B on AIME). Importantly, we do not prov...
| Authors | Ben Rank, Hardik Bhatnagar, Ameya Prabhu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 9 March 2026 |
| Last updated at source | 10 March 2026 |
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