AI agents can match frontier models at training other models

Gao and colleagues report that teaching agents with filtered examples of model training improved two models, with the larger matching selected frontier systems on post-training benchmarks.

Chinese Tech

Shuzheng Gao · Zhaojian Yu · Penghao Yin · Yuling Shi · Kai Cai · Shilin He · +1 more

The Chinese University of Hong Kong · ByteDance · Tsinghua University · Shanghai Jiao Tong University

Research Digest·4 November 2026·2 min read

Illustration: AI agents can match frontier models at training other models

In one line

Training LLM agents on RSI-Dojo trajectories improves PostTrainBench scores by 8.74 to 16.87 points and matches frontier model performance.

Before you read this

Three ideas this paper builds on

recursive self-improvementAn AI system repeatedly uses its own outputs to enhance its capabilities, creating a feedback loop for autonomous improvement.56 papers →large language modelA neural network trained on massive text corpora to generate coherent text and perform diverse language tasks.256 papers →agentic reinforcement learningTraining approach that lets LLM-based agents learn multi-step tool use and planning via reinforcement learning, often with dense or per-tool rewards.108 papers →

Read it as

For , the extra training that tailors them to specific tasks (called post-training) requires human expertise and is time-consuming. Automating this process could speed up AI development.

Researchers built a suite of training tasks from research papers, collected examples of successful post-training from advanced models, and kept only the best examples to train smaller models.

The trained models improved by 8.74 to 16.87 points on a standard benchmark, matching top models. This shows that automated post-training can be learned from quality examples.

How it works

stage by stage

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Concepts

recursive self-improvement large language model agentic reinforcement learning supervised fine-tuning agent harness synthetic data generation