{"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/turning-dust-into-gold-distilling-complex","title":"Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data","arxiv_id":"2312.12832","date":"2023-12-20","proceeding":null,"authors":["Yiwei Li","Peiwen Yuan","Shaoxiong Feng","Boyuan Pan","Bin Sun","Xinglin Wang","HeDa Wang","Kan Li"],"abstract":"Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising way is distilling the reasoning ability from LLMs to small models by the generated chain-of-thought reasoning paths. In some cases, however, LLMs may produce incorrect reasoning chains, especially when facing complex mathematical problems. Previous studies only transfer knowledge from positive samples and drop the synthesized data with wrong answers. In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones. The framework consists of three progressive steps, covering from training to inference stages, to absorb knowledge from negative data. We conduct extensive experiments across arithmetic reasoning tasks to demonstrate the role of negative data in distillation from LLM.","url_abs":"https://arxiv.org/abs/2312.12832v1","url_pdf":"https://arxiv.org/pdf/2312.12832v1.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":"turning-dust-into-gold-distilling-complex","repo_url":"https://github.com/Yiwei98/TDG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"arithmetic-reasoning","task_name":"Arithmetic Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.12832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12832"}},"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/Yiwei98/TDG","reach":{"status":"ok"}}],"summary":{"ran":6,"ran_draft_wrong":1,"unverified":2},"by_repo_kind":{"official":{"samples":9,"ran":7,"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":9,"samples":[{"code_sha256_prefix":"6906c93e3f8f8241","entry":"find_string_positions","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/data_pro.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/data_pro.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6906c93e3f8f8241"}},{"code_sha256_prefix":"76a4d5b991e6769e","entry":"get_algebra_examples","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/dataset.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/dataset.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"76a4d5b991e6769e"}},{"code_sha256_prefix":"b45ecd8e25d563f0","entry":"get_examples","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/dataset.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/dataset.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b45ecd8e25d563f0"}},{"code_sha256_prefix":"951d55a786237f31","entry":"read_jsonl","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/dataset.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/dataset.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"951d55a786237f31"}},{"code_sha256_prefix":"356accd290e81dd6","entry":"sentemb_forward","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/models_ft5.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/models_ft5.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"356accd290e81dd6"}},{"code_sha256_prefix":"b1f62aa72744913a","entry":"train_epoch","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/train_func_ft5.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/train_func_ft5.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b1f62aa72744913a"}},{"code_sha256_prefix":"bfe5ef5b92c1d61b","entry":"valid_epoch","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/train_func_ft5.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/train_func_ft5.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bfe5ef5b92c1d61b"}},{"code_sha256_prefix":"44fb54ab155aa892","entry":"cl_forward","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/models_ft5.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/models_ft5.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"44fb54ab155aa892"}},{"code_sha256_prefix":"fb5d9cda13ab4b08","entry":"ft_epoch","repo":"Yiwei98/TDG","repo_kind":"official","path":"code/RM/train_func_ft5.py","file_url":"https://github.com/Yiwei98/TDG/blob/HEAD/code/RM/train_func_ft5.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fb5d9cda13ab4b08"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}