by aisa-group · Codex Skill · ★ 425
PostTrainBench: Can LLM Agents Automate LLM Post-Training? We introduce PostTrainBench, a benchmark that measures the ability of CLI agents to post-train pre-trained large language models (LLMs). In PostTrainBench, the agent's task is to improve the performance of a base LLM on a given benchmark. The agent is given access to an evaluation script and 10 hours on an H100 GPU. Performance is measured by the benchmark score of the post-trained LLM. This setup naturally evaluates an agent's ability to conduct AI R&D.
| Stars | 425 |
| Forks | 55 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.4302057640871/100 |
| Open Issues | 16 |
| Last Updated | 2026-07-08 |
| Created | 2025-11-28 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~16k |
These tools work well together with PostTrainBench for enhanced workflows:
Explore other popular codex skill tools:
PostTrainBench is Measuring how well CLI agents like Claude Code or Codex CLI can post-train base LLMs on a single H100 GPU in 10 hours. It is categorized as a Codex Skill with 425 GitHub stars.
PostTrainBench is primarily written in Python. It covers topics such as ai-research-automation, ai-safety, claude-code.
You can find installation instructions and usage details in the PostTrainBench GitHub repository at github.com/aisa-group/PostTrainBench. The project has 425 stars and 55 forks, indicating an active community.
PostTrainBench is released under the MIT license, making it free to use and modify according to the license terms.