pith:C7Y3M7G4
$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
Coupling synthetic multi-hop questions with rewards for search steps and answers enables models to learn effective retrieval strategies and generalize up to 10% better out of domain.
arxiv:2605.01248 v3 · 2026-05-02 · cs.LG
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Record completeness
Claims
Our evaluations show that S^3-R1 outperforms existing baselines by learning more effective search and synthesis strategies, yielding up to a 10% improvement in robust generalization on out-of-domain datasets.
The synthetic generation and retrieval-based verification pipeline produces questions of genuinely intermediate difficulty that transfer to real user queries without introducing distribution shift or annotation artifacts that inflate measured gains.
S^3-R1 generates synthetic intermediate-difficulty multi-hop questions and applies dense rewards for search quality plus answer correctness, yielding up to 10% better out-of-domain generalization than baselines.
Receipt and verification
| First computed | 2026-06-10T01:08:36.073608Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
17f1b67cdcba8565158b199c89252a6df2339321e26e07bde1637fe1b0acbbd4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/C7Y3M7G4XKCWKFMLDGOISJJKNX \
| 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: 17f1b67cdcba8565158b199c89252a6df2339321e26e07bde1637fe1b0acbbd4
Canonical record JSON
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