AI

Training LLM Agents to Automate LLM Post-Training with RSI-Dojo

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 04/11/2026, 05:18:24 UTC.

Briefings

This research introduces RSI-Dojo, a collection of executable model training environments designed to teach Large Language Model (LLM) agents to conduct post-training. The system, built via RSI-Craft, aggregates 281 seed tasks from top research venues to create high-quality trajectories for model improvement. By fine-tuning Qwen3.5-9B and Qwen3.6-35B-A3B on these trajectories, the resulting models outperformed their base versions by 8.74 and 16.87 PostTrainBench points respectively, with the 35B variant reaching 29.95 on PostTrainBench and 53.29 on PostTrainBench-Lite.

Key points

• RSI-Dojo aggregates 281 seed model training tasks from top research venues to create executable environments for LLM agents.
• Fine-tuning Qwen3.5-9B and Qwen3.6-35B-A3B on RSI-Dojo trajectories improved performance by 8.74 and 16.87 PostTrainBench points respectively.

Context

Existing automated model training studies focused on workflow optimizations rather than internalizing post-training capabilities. This paper introduces RSI-Dojo to enable LLM agents to conduct post-training autonomously, addressing a gap in current automated model training approaches.

Limitations

Evidence is limited to a single preprint (arXiv:2611.03698v1) without peer review or experimental validation. Performance gains are measured on benchmark datasets and may not generalize to real-world scenarios. The study relies on synthetic trajectories generated from research artifacts rather than live model deployments.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 04/11/2026, 08:09:46 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeiap65gsefvxiwezyragrx5yiqn7mdy4kjwv34wmn6bbb7bo3jc4om