{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7OVGIHWBXMRKDBQSGZ45TY4UTK","short_pith_number":"pith:7OVGIHWB","canonical_record":{"source":{"id":"2405.20304","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T17:50:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9fda535b3d0de6e483609dd46d3b214b98b8620a8d70ae8953b6b0ea93df2f8a","abstract_canon_sha256":"915a76c12ae7cd4eabcb0d053dc1adc1738e413055990b7f953a2e759b9a06a8"},"schema_version":"1.0"},"canonical_sha256":"fbaa641ec1bb22a186123679d9e3949a8dc14a96c5d0e9f7280c452a85c0cc4b","source":{"kind":"arxiv","id":"2405.20304","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20304","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20304v1","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20304","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_12","alias_value":"7OVGIHWBXMRK","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_16","alias_value":"7OVGIHWBXMRKDBQS","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_8","alias_value":"7OVGIHWB","created_at":"2026-07-05T08:25:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7OVGIHWBXMRKDBQSGZ45TY4UTK","target":"record","payload":{"canonical_record":{"source":{"id":"2405.20304","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T17:50:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9fda535b3d0de6e483609dd46d3b214b98b8620a8d70ae8953b6b0ea93df2f8a","abstract_canon_sha256":"915a76c12ae7cd4eabcb0d053dc1adc1738e413055990b7f953a2e759b9a06a8"},"schema_version":"1.0"},"canonical_sha256":"fbaa641ec1bb22a186123679d9e3949a8dc14a96c5d0e9f7280c452a85c0cc4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:18.603542Z","signature_b64":"V3TP83bbePYl9O9hwTSOSdD3PMGIUM7iXFZw14W3kMVJvShiwWWLCQGiFa9iuKUZXVUGD8yWLnL/ckswnJV2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbaa641ec1bb22a186123679d9e3949a8dc14a96c5d0e9f7280c452a85c0cc4b","last_reissued_at":"2026-07-05T08:25:18.603125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:18.603125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.20304","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:25:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tl0vC8Scu2KBDAk9ERE/P07uJFqpWFvZT25LP268RCinXVjr6NogqmyyCXmDcJgOfLlB84ZYXFssCBCPDoaTDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:13:05.277468Z"},"content_sha256":"8fc3e11c63af60a4f2055905ec2d557996435476db0786e82c98e2a826eeccc3","schema_version":"1.0","event_id":"sha256:8fc3e11c63af60a4f2055905ec2d557996435476db0786e82c98e2a826eeccc3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7OVGIHWBXMRKDBQSGZ45TY4UTK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Group Robust Preference Optimization in Reward-free RLHF","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Haitham Bou Ammar, Iason Chaimalas, Ilija Bogunovic, Pier Giuseppe Sessa, Shyam Sundhar Ramesh, Viraj Mehta, Yifan Hu","submitted_at":"2024-05-30T17:50:04Z","abstract_excerpt":"Adapting large language models (LLMs) for specific tasks usually involves fine-tuning through reinforcement learning with human feedback (RLHF) on preference data. While these data often come from diverse labelers' groups (e.g., different demographics, ethnicities, company teams, etc.), traditional RLHF approaches adopt a \"one-size-fits-all\" approach, i.e., they indiscriminately assume and optimize a single preference model, thus not being robust to unique characteristics and needs of the various groups. To address this limitation, we propose a novel Group Robust Preference Optimization (GRPO)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20304","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/2405.20304/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:25:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"msdz01p3/UuzvG2FKtQX3FsUfQ5UWHSrHTyqb5WyajedEUyWfd39FrMieytUdK+n4g4wEaT/gyvlmP1/vH7pDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T08:13:05.277981Z"},"content_sha256":"f1a52c3e43ef77258efa062e02a1ebd570fb52c4060854de0f36babf76b1c08a","schema_version":"1.0","event_id":"sha256:f1a52c3e43ef77258efa062e02a1ebd570fb52c4060854de0f36babf76b1c08a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/bundle.json","state_url":"https://pith.science/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T08:13:05Z","links":{"resolver":"https://pith.science/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK","bundle":"https://pith.science/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/bundle.json","state":"https://pith.science/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7OVGIHWBXMRKDBQSGZ45TY4UTK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7OVGIHWBXMRKDBQSGZ45TY4UTK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"915a76c12ae7cd4eabcb0d053dc1adc1738e413055990b7f953a2e759b9a06a8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T17:50:04Z","title_canon_sha256":"9fda535b3d0de6e483609dd46d3b214b98b8620a8d70ae8953b6b0ea93df2f8a"},"schema_version":"1.0","source":{"id":"2405.20304","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20304","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20304v1","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20304","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_12","alias_value":"7OVGIHWBXMRK","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_16","alias_value":"7OVGIHWBXMRKDBQS","created_at":"2026-07-05T08:25:18Z"},{"alias_kind":"pith_short_8","alias_value":"7OVGIHWB","created_at":"2026-07-05T08:25:18Z"}],"graph_snapshots":[{"event_id":"sha256:f1a52c3e43ef77258efa062e02a1ebd570fb52c4060854de0f36babf76b1c08a","target":"graph","created_at":"2026-07-05T08:25:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.20304/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adapting large language models (LLMs) for specific tasks usually involves fine-tuning through reinforcement learning with human feedback (RLHF) on preference data. While these data often come from diverse labelers' groups (e.g., different demographics, ethnicities, company teams, etc.), traditional RLHF approaches adopt a \"one-size-fits-all\" approach, i.e., they indiscriminately assume and optimize a single preference model, thus not being robust to unique characteristics and needs of the various groups. To address this limitation, we propose a novel Group Robust Preference Optimization (GRPO)","authors_text":"Haitham Bou Ammar, Iason Chaimalas, Ilija Bogunovic, Pier Giuseppe Sessa, Shyam Sundhar Ramesh, Viraj Mehta, Yifan Hu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T17:50:04Z","title":"Group Robust Preference Optimization in Reward-free RLHF"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20304","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8fc3e11c63af60a4f2055905ec2d557996435476db0786e82c98e2a826eeccc3","target":"record","created_at":"2026-07-05T08:25:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"915a76c12ae7cd4eabcb0d053dc1adc1738e413055990b7f953a2e759b9a06a8","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T17:50:04Z","title_canon_sha256":"9fda535b3d0de6e483609dd46d3b214b98b8620a8d70ae8953b6b0ea93df2f8a"},"schema_version":"1.0","source":{"id":"2405.20304","kind":"arxiv","version":1}},"canonical_sha256":"fbaa641ec1bb22a186123679d9e3949a8dc14a96c5d0e9f7280c452a85c0cc4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbaa641ec1bb22a186123679d9e3949a8dc14a96c5d0e9f7280c452a85c0cc4b","first_computed_at":"2026-07-05T08:25:18.603125Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:18.603125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V3TP83bbePYl9O9hwTSOSdD3PMGIUM7iXFZw14W3kMVJvShiwWWLCQGiFa9iuKUZXVUGD8yWLnL/ckswnJV2CA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:18.603542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.20304","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8fc3e11c63af60a4f2055905ec2d557996435476db0786e82c98e2a826eeccc3","sha256:f1a52c3e43ef77258efa062e02a1ebd570fb52c4060854de0f36babf76b1c08a"],"state_sha256":"55c84db226d4f65e980641a026c71b5eca92e264a69ef4a844d92d6501e1d520"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"apUvvTazu/JqixoFQuJGeX3VMuygulPAtzr912rZx+dSsC291EXHoEFC3DZjitvgr8n1c5Yb9KEw/BV+fo9tBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T08:13:05.284023Z","bundle_sha256":"bbb6bc927ea728cd67dbc856f395b2887ca6ead802bf4e4ab1f51661ded7a76b"}}