pith:3IZOUWCO
Policy Improvement Reinforcement Learning
Reinforcement learning for language models can be made self-correcting by directly maximizing cumulative verified policy improvement across iterations.
arxiv:2604.00860 v3 · 2026-04-01 · cs.LG
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\usepackage{pith}
\pithnumber{3IZOUWCOH56UNY56EWZPMJU3YK}
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Record completeness
Claims
we prove this temporal objective is perfectly aligned with maximizing final task performance... PIPO performs ascent on the PIRL objective in expectation
that retrospective verification against a sliding-window historical baseline can reliably identify genuine policy improvement without bias from window size, data distribution, or the specific choice of baseline statistics
PIRL maximizes cumulative policy improvement across iterations instead of surrogate rewards and is proven aligned with final performance; PIPO implements it via retrospective verification for stable closed-loop optimization.
Receipt and verification
| First computed | 2026-06-04T01:08:49.541977Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
da32ea584e3f7d46e3be25b2f6269bc2b9b5a1e5d49c2155e2d81a4205ba76e0
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3IZOUWCOH56UNY56EWZPMJU3YK \
| 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())"
# expect: da32ea584e3f7d46e3be25b2f6269bc2b9b5a1e5d49c2155e2d81a4205ba76e0
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cs.LG",
"submitted_at": "2026-04-01T13:10:20Z",
"title_canon_sha256": "a2cd9e386953538d7261dc287c6ed1c4837d2d55f57212d33b2075867aa6df06"
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"source": {
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"kind": "arxiv",
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