{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6C67FSBQFLGUUCXZCRZOAIXKVD","short_pith_number":"pith:6C67FSBQ","schema_version":"1.0","canonical_sha256":"f0bdf2c8302acd4a0af91472e022eaa8ea267a605df6a61f20b018bf031ae2f0","source":{"kind":"arxiv","id":"2502.07211","version":1},"attestation_state":"computed","paper":{"title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiadong Yu, Xinren Zhang","submitted_at":"2025-02-11T03:09:45Z","abstract_excerpt":"Dynamic resource allocation in mobile wireless networks involves complex, time-varying optimization problems, motivating the adoption of deep reinforcement learning (DRL). However, most existing works rely on pre-trained policies, overlooking dynamic environmental changes that rapidly invalidate the policies. Periodic retraining becomes inevitable but incurs prohibitive computational costs and energy consumption-critical concerns for resource-constrained wireless systems. We identify three root causes of inefficient retraining: high-dimensional state spaces, suboptimal action spaces exploratio"},"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":"2502.07211","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-11T03:09:45Z","cross_cats_sorted":[],"title_canon_sha256":"3eeff39d5e8ae2b602d707ed756da46824cbb7cec78c77c1344eeaf8bd0d73be","abstract_canon_sha256":"9d891707bd73988419b875e7b2d4820a935fd1e3a7946cc7df035c8613a1e638"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:34.284974Z","signature_b64":"Tn93moQ5RDGNkZmK8usj1wBUFKKR0dUw+ctDiNjIJHOaQtLKXGtVbYq3NBKBXKTpM2q5xs8c3QiE7MhsPbBPBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0bdf2c8302acd4a0af91472e022eaa8ea267a605df6a61f20b018bf031ae2f0","last_reissued_at":"2026-07-05T10:12:34.284474Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:34.284474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiadong Yu, Xinren Zhang","submitted_at":"2025-02-11T03:09:45Z","abstract_excerpt":"Dynamic resource allocation in mobile wireless networks involves complex, time-varying optimization problems, motivating the adoption of deep reinforcement learning (DRL). However, most existing works rely on pre-trained policies, overlooking dynamic environmental changes that rapidly invalidate the policies. Periodic retraining becomes inevitable but incurs prohibitive computational costs and energy consumption-critical concerns for resource-constrained wireless systems. We identify three root causes of inefficient retraining: high-dimensional state spaces, suboptimal action spaces exploratio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.07211","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/2502.07211/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":"2502.07211","created_at":"2026-07-05T10:12:34.284540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.07211v1","created_at":"2026-07-05T10:12:34.284540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.07211","created_at":"2026-07-05T10:12:34.284540+00:00"},{"alias_kind":"pith_short_12","alias_value":"6C67FSBQFLGU","created_at":"2026-07-05T10:12:34.284540+00:00"},{"alias_kind":"pith_short_16","alias_value":"6C67FSBQFLGUUCXZ","created_at":"2026-07-05T10:12:34.284540+00:00"},{"alias_kind":"pith_short_8","alias_value":"6C67FSBQ","created_at":"2026-07-05T10:12:34.284540+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25531","citing_title":"From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD","json":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD.json","graph_json":"https://pith.science/api/pith-number/6C67FSBQFLGUUCXZCRZOAIXKVD/graph.json","events_json":"https://pith.science/api/pith-number/6C67FSBQFLGUUCXZCRZOAIXKVD/events.json","paper":"https://pith.science/paper/6C67FSBQ"},"agent_actions":{"view_html":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD","download_json":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD.json","view_paper":"https://pith.science/paper/6C67FSBQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.07211&json=true","fetch_graph":"https://pith.science/api/pith-number/6C67FSBQFLGUUCXZCRZOAIXKVD/graph.json","fetch_events":"https://pith.science/api/pith-number/6C67FSBQFLGUUCXZCRZOAIXKVD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD/action/storage_attestation","attest_author":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD/action/author_attestation","sign_citation":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD/action/citation_signature","submit_replication":"https://pith.science/pith/6C67FSBQFLGUUCXZCRZOAIXKVD/action/replication_record"}},"created_at":"2026-07-05T10:12:34.284540+00:00","updated_at":"2026-07-05T10:12:34.284540+00:00"}