Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

TMLR Paper2026 Authors

08 Jan 2024 (modified: 30 Mar 2024)Decision pending for TMLREveryoneRevisionsBibTeX
Abstract: Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call \method, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that \method{} scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can reduce dependence on human-generated data.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Tao_Qin1
Submission Number: 2026