CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
Large Language Models (LLMs) have recently exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training on high-quality reasoning data. However, reproducing and extending these capabilities in open and scalable settings is hindered by three fundamental data-centric challenges: (1) the cold-start problem, arising from the lack of seed datasets with detailed, long Chain-of-Thought (CoT) trajectories needed to initialize reasoning policies; (2) limited domain coverage, as most existing open-source reasoning datasets are concentrated in mathematics, with limited coverage of broader scientific disciplines; and...
| Authors | Xinyu Zhu, Yihao Feng, Yanchao Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
Models and datasets referencing this paper
- TianHongZXY/CHIMERADataset
- TianHongZXY/CHIMERA-4B-RLModel
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