{"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":"/code/get-global-statistics","entry":"get_global_statistics","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":5,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":3,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.18195","paper":"/paper/arxiv-2606-18195","title":"Learning from the Self-future: On-policy Self-distillation for dLLMs","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"xingzhejun/d-OPSD","path":"utils.py","file_url":"https://github.com/xingzhejun/d-OPSD/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2887fe30b3acdea7","mcp_get_code":{"code_sha256":"2887fe30b3acdea7"}},{"arxiv_id":"2504.10637","paper":"/paper/better-estimation-of-the-kl-divergence","title":"Better Estimation of the KL Divergence Between Language Models","date":"2025-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rycolab/kl-rb","path":"trl/trainer/utils.py","file_url":"https://github.com/rycolab/kl-rb/blob/HEAD/trl/trainer/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2887fe30b3acdea7","mcp_get_code":{"code_sha256":"2887fe30b3acdea7"}},{"arxiv_id":"2406.10977","paper":"/paper/toward-optimal-llm-alignments-using-two","title":"Toward Optimal LLM Alignments Using Two-Player Games","date":"2024-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruizheng20/gpo","path":"utils.py","file_url":"https://github.com/ruizheng20/gpo/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e1507eef3b40426e","mcp_get_code":{"code_sha256":"e1507eef3b40426e"}},{"arxiv_id":"2405.16681","paper":"/paper/triple-preference-optimization-achieving","title":"Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sahsaeedi/triple-preference-optimization","path":"utils/utils.py","file_url":"https://github.com/sahsaeedi/triple-preference-optimization/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d75c549406e58a87","mcp_get_code":{"code_sha256":"d75c549406e58a87"}},{"arxiv_id":"2401.06080","paper":"/paper/secrets-of-rlhf-in-large-language-models-part-1","title":"Secrets of RLHF in Large Language Models Part II: Reward Modeling","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openlmlab/moss-rlhf","path":"utils.py","file_url":"https://github.com/openlmlab/moss-rlhf/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e1507eef3b40426e","mcp_get_code":{"code_sha256":"e1507eef3b40426e"}}]}