DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911492061790208 |
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| author | Chen, Yicheng Ma, Zerun Xie, Xinchen Li, Yining Chen, Kai |
| author_facet | Chen, Yicheng Ma, Zerun Xie, Xinchen Li, Yining Chen, Kai |
| contents | In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the \emph{data recipe}, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to automate individual data processing steps, such as data synthesis and filtering, the overall design of data recipes remains largely manual and labor-intensive, requiring substantial human expertise and iteration. To bridge this gap, we formulate \emph{end-to-end data recipe generation} for LLM adaptation. Given a target benchmark and a pool of available data sources, a model is required to output a complete data recipe that adapts a base LLM to the target task. We present DataChef-32B, which performs online reinforcement learning using a proxy reward that predicts downstream performance for candidate recipes. Across six held-out tasks, DataChef-32B produces recipes that yield performance comparable to those curated by human experts. Notably, the recipe from DataChef-32B adapts Qwen3-1.7B-Base to the math domain, achieving 66.7 on AIME'25 and surpassing the official post-training checkpoint (Qwen3-1.7B). This work sheds new light on automating LLM training and developing self-evolving AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11089 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning Chen, Yicheng Ma, Zerun Xie, Xinchen Li, Yining Chen, Kai Computation and Language Artificial Intelligence In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the \emph{data recipe}, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to automate individual data processing steps, such as data synthesis and filtering, the overall design of data recipes remains largely manual and labor-intensive, requiring substantial human expertise and iteration. To bridge this gap, we formulate \emph{end-to-end data recipe generation} for LLM adaptation. Given a target benchmark and a pool of available data sources, a model is required to output a complete data recipe that adapts a base LLM to the target task. We present DataChef-32B, which performs online reinforcement learning using a proxy reward that predicts downstream performance for candidate recipes. Across six held-out tasks, DataChef-32B produces recipes that yield performance comparable to those curated by human experts. Notably, the recipe from DataChef-32B adapts Qwen3-1.7B-Base to the math domain, achieving 66.7 on AIME'25 and surpassing the official post-training checkpoint (Qwen3-1.7B). This work sheds new light on automating LLM training and developing self-evolving AI systems. |
| title | DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2602.11089 |