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pith:2026:IOVTADSAIQNRQIZOM5ICML5RTE
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Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models

Jeongjae Lee, Jeongsol Kim, Jinho Chang, Jong Chul Ye

Many reward-based fine-tuning methods for diffusion and flow models reduce to a single score-matching objective against a value-guided target.

arxiv:2604.17415 v3 · 2026-04-19 · cs.LG · cs.AI · cs.CV

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Claims

C1strongest claim

Although existing methods are derived from different perspectives, we show that many can be written under a common framework, which we call reward score matching (RSM).

C2weakest assumption

That the primary distinctions among existing methods reduce to the construction of the value-guidance estimator and the effective optimization strength across timesteps, without material loss of generality or overlooked auxiliary mechanisms.

C3one line summary

Reward Score Matching unifies reward-based fine-tuning for flow and diffusion models by recasting alignment as score matching to a value-guided target.

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First computed 2026-06-02T01:03:47.239586Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

43ab300e40441b18232e6750262fb19903d11d9b06c5390de10b5769fee83e8f

Aliases

arxiv: 2604.17415 · arxiv_version: 2604.17415v3 · doi: 10.48550/arxiv.2604.17415 · pith_short_12: IOVTADSAIQNR · pith_short_16: IOVTADSAIQNRQIZO · pith_short_8: IOVTADSA
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IOVTADSAIQNRQIZOM5ICML5RTE \
  | 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: 43ab300e40441b18232e6750262fb19903d11d9b06c5390de10b5769fee83e8f
Canonical record JSON
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    "submitted_at": "2026-04-19T12:47:52Z",
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