{"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/convert-to-regular-types","entry":"convert_to_regular_types","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":9,"n_papers_ran":8,"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":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":1},"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":"2602.15564","paper":"/paper/arxiv-2602-15564","title":"Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Satissss/SquRL","path":"verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/Satissss/SquRL/blob/HEAD/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2602.12735","paper":"/paper/arxiv-2602-12735","title":"VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Alibaba-NLP/VRAG","path":"VRAG-RL/verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/Alibaba-NLP/VRAG/blob/HEAD/VRAG-RL/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2602.04284","paper":"/paper/arxiv-2602-04284","title":"Agent-Omit: Adaptive Context Omission for Efficient LLM Agents","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"usail-hkust/Agent-Omit","path":"AgentOmit-RL/verl/agent_trainer/fsdp_sft_trainer.py","file_url":"https://github.com/usail-hkust/Agent-Omit/blob/HEAD/AgentOmit-RL/verl/agent_trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2505.24630","paper":"/paper/the-hallucination-dilemma-factuality-aware","title":"The Hallucination Dilemma: Factuality-Aware Reinforcement Learning for Large Reasoning Models","date":"2025-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nusnlp/fspo","path":"verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/nusnlp/fspo/blob/HEAD/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2505.18917","paper":"/paper/behavior-injection-preparing-language-models","title":"Behavior Injection: Preparing Language Models for Reinforcement Learning","date":"2025-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"czp16/bridge-llm-reasoning","path":"simple_verl/sverl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/czp16/bridge-llm-reasoning/blob/HEAD/simple_verl/sverl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2505.04588","paper":"/paper/zerosearch-incentivize-the-search-capability","title":"ZeroSearch: Incentivize the Search Capability of LLMs without Searching","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba-nlp/zerosearch","path":"verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/alibaba-nlp/zerosearch/blob/HEAD/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2504.05520","paper":"/paper/efficient-reinforcement-finetuning-via","title":"Efficient Reinforcement Finetuning via Adaptive Curriculum Learning","date":"2025-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uscnlp-lime/verl","path":"verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/uscnlp-lime/verl/blob/HEAD/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"2410.21236","paper":"/paper/flaming-hot-initiation-with-regular-execution","title":"Flaming-hot Initiation with Regular Execution Sampling for Large Language Models","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaof20/real","path":"verl/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/yaof20/real/blob/HEAD/verl/trainer/fsdp_sft_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f887d72c9492d9ee","mcp_get_code":{"code_sha256":"f887d72c9492d9ee"}},{"arxiv_id":"openreview_QrC8OgQyOI","paper":null,"title":"arXiv:openreview_QrC8OgQyOI","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"viiika/Prism","path":"Dream/Dream_Prism/src/trainer/fsdp_sft_trainer.py","file_url":"https://github.com/viiika/Prism/blob/HEAD/Dream/Dream_Prism/src/trainer/fsdp_sft_trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0aac3c307ba4a07b","mcp_get_code":{"code_sha256":"0aac3c307ba4a07b"}}]}