VibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System Improvement
We introduce VibeTraining-Bench, a benchmark for evaluating this capability across tasks spanning terminal operation, software engineering, multi-turn tool use, and mathematical reasoning.
ProofPaper ↗
Key points
- Recursive self-improvement (RSI) requires agents to diagnose system bottlenecks and coordinate improvements across training and inference.
- We refer to the agents' capability to jointly optimize training data, the trainer, and the inference harness as cross-component co-design.
- Our evaluation results show that frontier research agents substantially improve target agent systems.
- We hope that VibeTraining-Bench will serve as a unified platform for advancing RSI, deepening our understanding of cross-component optimization for agent systems.
Sources (1)
- [1]VibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System ImprovementarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:10 PM
We introduce VibeTraining-Bench, a benchmark for evaluating this capability across tasks spanning terminal operation, software engineering, multi-turn tool use, and mathematical reasoning.
Recursive self-improvement (RSI) requires agents to diagnose system bottlenecks and coordinate improvements across training and inference.
Extractive summary: sentences quoted from the sources.
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