{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T3GPDLNFD5EDOX5BWKP72QIONE","short_pith_number":"pith:T3GPDLNF","schema_version":"1.0","canonical_sha256":"9eccf1ada51f48375fa1b29ffd410e69252ea769642c1a3b605914cff39b8eaf","source":{"kind":"arxiv","id":"2401.07382","version":2},"attestation_state":"computed","paper":{"title":"Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Lei Meng, Lei Shu, Lei Yu, Meng Cao, Nevan Wichers, Yinxiao Liu, Yun Zhu","submitted_at":"2024-01-14T22:05:11Z","abstract_excerpt":"Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only a single reward for an entire output. This sparsity of rewards can lead to inefficient and unstable learning. To address this challenge, our paper introduces an novel framework that utilizes the critique capability of Large Language Models (LLMs) to produce intermediate-step rewards during RL training. Our method involves coupling a policy model with a critic language model"},"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":"2401.07382","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-14T22:05:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"17420fea09325296a55a18ec04e8618f9ccf6e83890920e904bcc50d7ca6d199","abstract_canon_sha256":"a402d663d78ae53eb762403d3ca82532548f1cb3fa730534eb329b4bdc8d7b46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:29.591728Z","signature_b64":"lhYfPUFST8uPyqMokAfPQpOFIy2sa3liz7h79spjL237UwWKLdTAN5HVKY5r/R9d1R4WDtQtOJ6M0vDcISmEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9eccf1ada51f48375fa1b29ffd410e69252ea769642c1a3b605914cff39b8eaf","last_reissued_at":"2026-07-05T07:46:29.591155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:29.591155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Lei Meng, Lei Shu, Lei Yu, Meng Cao, Nevan Wichers, Yinxiao Liu, Yun Zhu","submitted_at":"2024-01-14T22:05:11Z","abstract_excerpt":"Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only a single reward for an entire output. This sparsity of rewards can lead to inefficient and unstable learning. To address this challenge, our paper introduces an novel framework that utilizes the critique capability of Large Language Models (LLMs) to produce intermediate-step rewards during RL training. Our method involves coupling a policy model with a critic language model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.07382","kind":"arxiv","version":2},"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/2401.07382/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":"2401.07382","created_at":"2026-07-05T07:46:29.591223+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.07382v2","created_at":"2026-07-05T07:46:29.591223+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.07382","created_at":"2026-07-05T07:46:29.591223+00:00"},{"alias_kind":"pith_short_12","alias_value":"T3GPDLNFD5ED","created_at":"2026-07-05T07:46:29.591223+00:00"},{"alias_kind":"pith_short_16","alias_value":"T3GPDLNFD5EDOX5B","created_at":"2026-07-05T07:46:29.591223+00:00"},{"alias_kind":"pith_short_8","alias_value":"T3GPDLNF","created_at":"2026-07-05T07:46:29.591223+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21943","citing_title":"Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08346","citing_title":"CATPO: Critique-Augmented Tree Policy Optimization","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12289","citing_title":"PriorZero: Bridging Language Priors and World Models for Decision Making","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE","json":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE.json","graph_json":"https://pith.science/api/pith-number/T3GPDLNFD5EDOX5BWKP72QIONE/graph.json","events_json":"https://pith.science/api/pith-number/T3GPDLNFD5EDOX5BWKP72QIONE/events.json","paper":"https://pith.science/paper/T3GPDLNF"},"agent_actions":{"view_html":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE","download_json":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE.json","view_paper":"https://pith.science/paper/T3GPDLNF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.07382&json=true","fetch_graph":"https://pith.science/api/pith-number/T3GPDLNFD5EDOX5BWKP72QIONE/graph.json","fetch_events":"https://pith.science/api/pith-number/T3GPDLNFD5EDOX5BWKP72QIONE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE/action/storage_attestation","attest_author":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE/action/author_attestation","sign_citation":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE/action/citation_signature","submit_replication":"https://pith.science/pith/T3GPDLNFD5EDOX5BWKP72QIONE/action/replication_record"}},"created_at":"2026-07-05T07:46:29.591223+00:00","updated_at":"2026-07-05T07:46:29.591223+00:00"}