Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.18273 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908918057271296 |
|---|---|
| author | Wang, Ru Huang, Wei Song, Selena Zhang, Haoyu Niu, Qian Iwasawa, Yusuke Matsuo, Yutaka Guo, Jiaxian |
| author_facet | Wang, Ru Huang, Wei Song, Selena Zhang, Haoyu Niu, Qian Iwasawa, Yusuke Matsuo, Yutaka Guo, Jiaxian |
| contents | Generalization to novel compound tasks under distribution shift is important for deploying transformer-based language models (LMs). This work investigates Chain-of-Thought (CoT) reasoning as a means to enhance OOD generalization. Through controlled experiments across several compound tasks, we reveal three key insights: (1) While QA-trained models achieve near-perfect in-distribution accuracy, their OOD performance degrades catastrophically, even with 10000k+ training examples; (2) the granularity of CoT data strongly correlates with generalization performance; finer-grained CoT data leads to better generalization; (3) CoT exhibits remarkable sample efficiency, matching QA performance with much less (even 80%) data.
Theoretically, we demonstrate that compound tasks inherently permit shortcuts in Q-A data that misalign with true reasoning principles, while CoT forces internalization of valid dependency structures, and thus can achieve better generalization. Further, we show that transformer positional embeddings can amplify generalization by emphasizing subtask condition recurrence in long CoT sequences. Our combined theoretical and empirical analysis provides compelling evidence for CoT reasoning as a crucial training paradigm for enabling LM generalization under real-world distributional shifts for compound tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization Wang, Ru Huang, Wei Song, Selena Zhang, Haoyu Niu, Qian Iwasawa, Yusuke Matsuo, Yutaka Guo, Jiaxian Computation and Language Generalization to novel compound tasks under distribution shift is important for deploying transformer-based language models (LMs). This work investigates Chain-of-Thought (CoT) reasoning as a means to enhance OOD generalization. Through controlled experiments across several compound tasks, we reveal three key insights: (1) While QA-trained models achieve near-perfect in-distribution accuracy, their OOD performance degrades catastrophically, even with 10000k+ training examples; (2) the granularity of CoT data strongly correlates with generalization performance; finer-grained CoT data leads to better generalization; (3) CoT exhibits remarkable sample efficiency, matching QA performance with much less (even 80%) data. Theoretically, we demonstrate that compound tasks inherently permit shortcuts in Q-A data that misalign with true reasoning principles, while CoT forces internalization of valid dependency structures, and thus can achieve better generalization. Further, we show that transformer positional embeddings can amplify generalization by emphasizing subtask condition recurrence in long CoT sequences. Our combined theoretical and empirical analysis provides compelling evidence for CoT reasoning as a crucial training paradigm for enabling LM generalization under real-world distributional shifts for compound tasks. |
| title | Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.18273 |