RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement
Recursive self-improvement requires turning evidence of model failures into better models. Data-centric post-training research entails diagnosing capability gaps, designing and validating training-data strategies, and learning from checkpoint feedback. Can LLM agents automate this loop? Existing benchmarks entangle research decisions with optimization, serving, evaluation, and systems implementation, obscuring agents' research capability. We introduce RSIBench-Data, a controlled benchmark of LLM agents as data-centric researchers with a fixed post-training stack. Agents iteratively revise training-data strategies for a fixed target model; training and serving use Tinker-backed services, o...
| Authors | Fanqing Meng, Lingxiao Du, Qiguang Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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