{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZAK6A7DIVL3OUTXB6GUJJX7VWB","short_pith_number":"pith:ZAK6A7DI","schema_version":"1.0","canonical_sha256":"c815e07c68aaf6ea4ee1f1a894dff5b061c7d6a915dc8f0601767e25079692e3","source":{"kind":"arxiv","id":"2606.28998","version":1},"attestation_state":"computed","paper":{"title":"Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Gias Uddin, Sanjeepan Sivapiran","submitted_at":"2026-06-27T16:22:22Z","abstract_excerpt":"Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards. While LLM alignment techniques are studied for non-coding tasks, we know little about their usefulness for coding tasks. It is unclear whether LLM code alignment could support both functional requirements (producing executable, correct code) and non-functional requirements (code readability, style, maintainability). It is also unknown whether alignment for a code LLM should begin with base pretrained version or the finetuned (i.e., instruction-tuned) versi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.28998","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-27T16:22:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"30dfdb99102f4ab9260a4f4386d218e86ecf298be087059e3b19547a29edc4e8","abstract_canon_sha256":"395d5b9fa22be4dbaf86fd358422e41d9d13b450a11aa570e12392ba4b8e915b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:48.816398Z","signature_b64":"MZo41LJVhgXcK3aaKR0+FH77n8xF6tF5LrWNLg1HbBpkP78c9q0/KuK0A1O1SoD5OmGt0vJlnegDwG1lIJwsAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c815e07c68aaf6ea4ee1f1a894dff5b061c7d6a915dc8f0601767e25079692e3","last_reissued_at":"2026-06-30T01:17:48.815901Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:48.815901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Gias Uddin, Sanjeepan Sivapiran","submitted_at":"2026-06-27T16:22:22Z","abstract_excerpt":"Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards. While LLM alignment techniques are studied for non-coding tasks, we know little about their usefulness for coding tasks. It is unclear whether LLM code alignment could support both functional requirements (producing executable, correct code) and non-functional requirements (code readability, style, maintainability). It is also unknown whether alignment for a code LLM should begin with base pretrained version or the finetuned (i.e., instruction-tuned) versi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28998","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.28998/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.28998","created_at":"2026-06-30T01:17:48.815994+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.28998v1","created_at":"2026-06-30T01:17:48.815994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28998","created_at":"2026-06-30T01:17:48.815994+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZAK6A7DIVL3O","created_at":"2026-06-30T01:17:48.815994+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZAK6A7DIVL3OUTXB","created_at":"2026-06-30T01:17:48.815994+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZAK6A7DI","created_at":"2026-06-30T01:17:48.815994+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB","json":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB.json","graph_json":"https://pith.science/api/pith-number/ZAK6A7DIVL3OUTXB6GUJJX7VWB/graph.json","events_json":"https://pith.science/api/pith-number/ZAK6A7DIVL3OUTXB6GUJJX7VWB/events.json","paper":"https://pith.science/paper/ZAK6A7DI"},"agent_actions":{"view_html":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB","download_json":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB.json","view_paper":"https://pith.science/paper/ZAK6A7DI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.28998&json=true","fetch_graph":"https://pith.science/api/pith-number/ZAK6A7DIVL3OUTXB6GUJJX7VWB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZAK6A7DIVL3OUTXB6GUJJX7VWB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB/action/storage_attestation","attest_author":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB/action/author_attestation","sign_citation":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB/action/citation_signature","submit_replication":"https://pith.science/pith/ZAK6A7DIVL3OUTXB6GUJJX7VWB/action/replication_record"}},"created_at":"2026-06-30T01:17:48.815994+00:00","updated_at":"2026-06-30T01:17:48.815994+00:00"}