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ThetaEvolve: Test-time Learning on Open Problems

Baolin Peng, Eva Xu, Hao Cheng, Liliang Ren, Luyao Ma, Pengcheng He, Shao-Rong Su, Shuohang Wang, Simon Shaolei Du, Weizhu Chen, Xinyu Yang, Xuehai He, Yelong Shen, Yiping Wang, Zeyi Huang, Zhiyuan Zeng

A small open-source model learns to evolve programs at test time and sets new best-known bounds on open mathematical problems.

arxiv:2511.23473 v1 · 2025-11-28 · cs.LG · cs.CL

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Claims

C1strongest claim

ThetaEvolve is the first evolving framework that enable a small open-source model, like DeepSeek-R1-0528-Qwen3-8B, to achieve new best-known bounds on open problems (circle packing and first auto-correlation inequality) mentioned in AlphaEvolve.

C2weakest assumption

That the observed improvements and cross-task transfer result from the model internalizing evolving strategies via RL rather than from increased total compute, specific hyperparameter choices, or the particular program database construction.

C3one line summary

ThetaEvolve enables small open-source LLMs to achieve new best-known bounds on open problems such as circle packing by combining test-time RL with a large program database and lazy penalties.

References

50 extracted · 50 resolved · 2 Pith anchors

[1] Spurious Rewards: Rethinking Training Signals in RLVR 2024 · arXiv:2506.10947
[2] The optimal arrangement likely involves variable-sized circles
[3] A pure hexagonal arrangement may not be optimal due to edge effects
[4] The densest known circle packings often use a hybrid approach
[5] The optimization routine is critically important - simple physics-based models with carefully tuned parameters

Formal links

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Cited by

21 papers in Pith

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c7b65969c0309c42d963e70dc758ab87abd9d741857088a373183e4422af9a11

Aliases

arxiv: 2511.23473 · arxiv_version: 2511.23473v1 · doi: 10.48550/arxiv.2511.23473 · pith_short_12: Y63FS2OAGCOE · pith_short_16: Y63FS2OAGCOEFWLD · pith_short_8: Y63FS2OA
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y63FS2OAGCOEFWLD44G4OWFLQ6 \
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Canonical record JSON
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