{"paper":{"title":"Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Training a prefix value function on outcome labels alone produces reliable step rewards for reasoning chains.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hongzhan Chen, Lifu Huang, Qifan Wang, Shiping Gao, Xiaojun Quan","submitted_at":"2026-04-14T18:19:54Z","abstract_excerpt":"Process reward models (PRMs) provide fine-grained supervision for reasoning, but reliable PRMs often require step annotations or heavy verification pipelines, making them costly to scale and refresh during online RL. Implicit PRMs reduce this cost by training log-likelihood-ratio rewards from trajectory-level outcome labels. However, the log-ratio is constrained only as a sequence-level aggregate during training, while inference decomposes it into token- or step-level scores for partial prefixes. This train-inference mismatch leaves local credits weakly identified, so distribution-wide scoring"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"IPVRM substantially improves step-verification F1 on ProcessBench. Building on these calibrated prefix values, we further propose Distribution-Level RL (DistRL), which ... consistently improves downstream reasoning once paired with IPVRM.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a prefix-conditioned value function trained only on outcome labels can accurately estimate the probability of eventual correctness and that TD differences between prefixes yield faithful local step-quality signals without systematic bias or additional verification.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"IPVRM learns prefix values to produce reliable step rewards from sequence outcomes using TD learning, enabling distribution-level RL that improves reasoning when paired with calibrated rewards.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Training a prefix value function on outcome labels alone produces reliable step rewards for reasoning chains.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3bbfd9c4a2e3a242312b4552794b65d42dbf82c1686dcb7511f11afab6499d7d"},"source":{"id":"2604.13197","kind":"arxiv","version":2},"verdict":{"id":"e24ca9a9-0f97-4abc-8cbd-eb1f9c0b5345","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:41:34.582605Z","strongest_claim":"IPVRM substantially improves step-verification F1 on ProcessBench. Building on these calibrated prefix values, we further propose Distribution-Level RL (DistRL), which ... consistently improves downstream reasoning once paired with IPVRM.","one_line_summary":"IPVRM learns prefix values to produce reliable step rewards from sequence outcomes using TD learning, enabling distribution-level RL that improves reasoning when paired with calibrated rewards.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a prefix-conditioned value function trained only on outcome labels can accurately estimate the probability of eventual correctness and that TD differences between prefixes yield faithful local step-quality signals without systematic bias or additional verification.","pith_extraction_headline":"Training a prefix value function on outcome labels alone produces reliable step rewards for reasoning chains."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.13197/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"}