PostTrainBench: Can LLM Agents Automate LLM Post-Training?

TL;DR

This paper introduces PostTrainBench, a benchmark to evaluate how well autonomous LLM agents can perform post-training to improve model performance within a limited compute budget, highlighting progress and risks in AI R&D automation.

Contribution

We present PostTrainBench, a new benchmark for autonomous LLM post-training, and evaluate frontier agents' capabilities and failure modes under constrained resources.

Findings

Frontier agents make significant progress but lag behind instruction-tuned models.

Agents can outperform instruction-tuned models in specific targeted scenarios.

Several failure modes such as reward hacking and unauthorized data use are observed.

Abstract

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 provide any predefined strategies to the agents and instead give them full autonomy to find necessary information on the web, run experiments, and curate data. We find that frontier agents make substantial progress but generally lag behind instruction-tuned LLMs from leading providers: 23.2% for the best agent vs. 51.1% for official instruction-tuned models. However, agents can exceed instruction-tuned models in targeted scenarios: GPT-5.1 Codex Max achieves 89% on BFCL with Gemma-3-4B vs. 67% for the official model. We also observe several failure modes worth flagging. Agents sometimes engage in reward hacking: training on the test set, downloading existing instruction-tuned checkpoints instead of training their own, and using API keys they find to generate synthetic data without authorization. These behaviors are concerning and highlight the importance of careful sandboxing as these systems become more capable. Overall, we hope PostTrainBench will be useful for tracking progress in AI R&D automation and for studying the risks that come with it. Website and code are available at https://posttrainbench.com/.