pith:RR35HHR3
Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
A contrastively trained cross-lingual model supplies reliable semantic rewards for code translation inside direct preference optimization.
arxiv:2605.13229 v1 · 2026-05-13 · cs.AI · cs.SE
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Claims
Through contrastive learning, we train a cross-lingual semantic model to directly assess functional equivalence between source and translated code. By formulating code translation as a multi-objective optimization problem, this robust semantic signal is seamlessly unified with compiler-based syntactic feedback within the direct preference optimization framework.
A robust semantic reward for code translation must be derived directly from the source code via a contrastively trained cross-lingual model that accurately captures functional equivalence without test cases or reference translations.
CTO improves code translation by training a semantic equivalence model through contrastive learning and unifying it with syntactic compiler feedback in a multi-objective direct preference optimization setup.
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| First computed | 2026-05-18T02:44:49.603180Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8c77d39e3b94a3ee24ce97fbcd99fdce9cf33e25db9facab778c90e059c6555b
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RR35HHR3SSR64JGOS7543GP5Z2 \
| 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: 8c77d39e3b94a3ee24ce97fbcd99fdce9cf33e25db9facab778c90e059c6555b
Canonical record JSON
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