pith:Y7KS2YN2
Learning to Reason without External Rewards
Large language models can improve at reasoning by using only their own internal confidence as the reward signal.
arxiv:2505.19590 v4 · 2025-05-26 · cs.LG · cs.CL
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\pithnumber{Y7KS2YN22NQU7VINAEUXTHR2TV}
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Record completeness
Claims
Experiments demonstrate that Intuitor matches GRPO's performance on mathematical benchmarks while achieving better generalization to out-of-domain tasks like code generation, without requiring gold solutions or test cases.
That the model's self-certainty score reliably indicates correct reasoning and does not encourage reward hacking or overconfidence in incorrect outputs.
Intuitor enables LLMs to learn complex reasoning from self-certainty signals alone, matching supervised RL performance on math benchmarks while generalizing better to code generation without gold solutions or test cases.
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Cited by
Receipt and verification
| First computed | 2026-05-17T23:38:50.140295Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c7d52d61bad3614fd50d0129799e3a9d6ff56e1d00ca57a05aa0c5c065316e4a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y7KS2YN22NQU7VINAEUXTHR2TV \
| 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: c7d52d61bad3614fd50d0129799e3a9d6ff56e1d00ca57a05aa0c5c065316e4a
Canonical record JSON
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