{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/critique-fine-tuning-learning-to-critique-is","title":"Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate","arxiv_id":"2501.17703","date":"2025-01-29","proceeding":null,"authors":["YuBo Wang","Xiang Yue","Wenhu Chen"],"abstract":"Supervised Fine-Tuning (SFT) is commonly used to train language models to imitate annotated responses for given instructions. In this paper, we propose Critique Fine-Tuning (CFT), a method more effective than SFT for reasoning tasks. Instead of simply imitating correct responses, CFT trains models to critique noisy responses, inspired by human learning processes that emphasize critical thinking, deeper analysis, and nuanced understanding - traits often overlooked by standard SFT. To validate the effectiveness of CFT, we construct multiple critique datasets (e.g., WebInstruct, MetaMath, NuminaMath), where GPT-4o serves as the teacher to generate critiques in the form of ([query; noisy response], critique). Experiments on these datasets demonstrate that CFT consistently outperforms SFT by 4-10% across six mathematical reasoning benchmarks, and is effective across different base models including Qwen2.5, Qwen2.5-Math, and DeepSeek-Math. Notably, our model Qwen2.5-Math-CFT only requires 1 hour of training on 8 x H100 over the 50K examples, yet matches or outperforms strong competitors like Qwen2.5-Math-Instruct on most benchmarks, which use over 2M samples. Moreover, it matches the performance of SimpleRL, which is a DeepSeek-r1 replication trained with 140 x more compute. Experiments on IF_Eval and MT-Bench further demonstrate that CFT can significantly enhance the model's general generation and instruction-following capabilities, outperforming the Qwen2.5-Math-Instruct by a large margin. Ablation studies show that CFT is robust to noisy response sources and teacher critique models. These findings highlight that CFT offers a more effective alternative to advance the reasoning of language models.","url_abs":"https://arxiv.org/abs/2501.17703v4","url_pdf":"https://arxiv.org/pdf/2501.17703v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"critique-fine-tuning-learning-to-critique-is","repo_url":"https://github.com/TIGER-AI-Lab/CritiqueFineTuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"instruction-following","task_name":"Instruction Following"},{"task_slug":"math","task_name":"Math"},{"task_slug":"mathematical-reasoning","task_name":"Mathematical Reasoning"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"sft","method_name":"SFT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2501.17703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.17703"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/TIGER-AI-Lab/CritiqueFineTuning","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"listed":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"dbea5358be6b5ad0","entry":"create_score_evaluation_response","repo":"TIGER-AI-Lab/CritiqueFineTuning","repo_kind":"listed","path":"train/LLaMA-Factory/src/llamafactory/api/chat.py","file_url":"https://github.com/TIGER-AI-Lab/CritiqueFineTuning/blob/HEAD/train/LLaMA-Factory/src/llamafactory/api/chat.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dbea5358be6b5ad0"}},{"code_sha256_prefix":"57583cf880d95444","entry":"jsonify","repo":"TIGER-AI-Lab/CritiqueFineTuning","repo_kind":"listed","path":"train/LLaMA-Factory/src/llamafactory/api/common.py","file_url":"https://github.com/TIGER-AI-Lab/CritiqueFineTuning/blob/HEAD/train/LLaMA-Factory/src/llamafactory/api/common.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"57583cf880d95444"}},{"code_sha256_prefix":"87aa9acaa81294f5","entry":"dictify","repo":"TIGER-AI-Lab/CritiqueFineTuning","repo_kind":"listed","path":"train/LLaMA-Factory/src/llamafactory/api/common.py","file_url":"https://github.com/TIGER-AI-Lab/CritiqueFineTuning/blob/HEAD/train/LLaMA-Factory/src/llamafactory/api/common.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"87aa9acaa81294f5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}