pith:2LM7DJDE
Learning to Discover at Test Time
Reinforcement learning at test time on one problem lets an open LLM produce new state-of-the-art solutions for math, coding, and biology tasks.
arxiv:2601.16175 v2 · 2026-01-22 · cs.LG · cs.AI
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\pithnumber{2LM7DJDEYTMMXBDHWCTPR2RQK2}
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Claims
TTT-Discover sets the new state of the art in almost all of them: (i) Erdős' minimum overlap problem and an autocorrelation inequality; (ii) a GPUMode kernel competition (up to 2× faster than prior art); (iii) past AtCoder algorithm competitions; and (iv) denoising problem in single-cell analysis.
That reinforcement learning performed at test time on experience specific to one problem will reliably produce a single superior solution rather than overfitting or failing to improve over frozen-model search.
TTT-Discover applies test-time RL to set new state-of-the-art results on math inequalities, GPU kernels, algorithm contests, and single-cell denoising using an open model and public code.
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Receipt and verification
| First computed | 2026-05-17T23:38:48.999463Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d2d9f1a464c4d8cb8467b0a6f8ea305696999db0620761966731b8ed3fa2b765
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2LM7DJDEYTMMXBDHWCTPR2RQK2 \
| 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: d2d9f1a464c4d8cb8467b0a6f8ea305696999db0620761966731b8ed3fa2b765
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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