pith:VBJO4ENL
Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
Training a prefix value function on outcome labels alone produces reliable step rewards for reasoning chains.
arxiv:2604.13197 v2 · 2026-04-14 · cs.CL
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\pithnumber{VBJO4ENLCH2POJSNTJTSR2I5I5}
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Claims
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.
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.
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.
Receipt and verification
| First computed | 2026-05-29T01:05:09.674642Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/VBJO4ENLCH2POJSNTJTSR2I5I5 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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