aiqbench analysis
This arXiv preprint proposes Bidirectional Evolutionary Search, a method that pairs evolutionary recombination of partial reasoning trajectories with recursive decomposition of tasks into checkable sub-goals, to improve language model post-training and inference beyond best-of-N sampling and tree search. The reported gains are on specific math and competitive programming benchmarks, and the paper's claims about escaping a "narrow entropy shell" are motivational rather than broadly validated. Readers building reasoning or post-training pipelines should watch whether the approach reproduces outside these settings and at comparable compute cost.
Self-Improving Language Models with Bidirectional Evolutionary Search
arXiv:2605.28814v2 Announce Type: replace Abstract: Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely used methods such as best-of-N sampling and tree search face two fundamental limitations: they are guided by sparse verification signals, and they construct candidates primarily through autoregressive expansion, restricting exploration to regions with substantial model probability mass. To address these, we propose Bidirectional Evolutionary Search (BES), a search framework that couples forward candidate evolution with backward goal decomposition. In the forward search, BES augments standard expansion with evolution operators that recombine partial trajectories to generate candidates that are difficult to obtain from a single model rollout. In the backward search, BES recursively decomposes the original task into checkable sub-goals, producing dense intermediate feedback that guides forward search. We provide motivation illustrating that candidates generated by expansion-only search may be confined to a narrow entropy shell, while evolution operators may escape it. Experiments show that on challenging post-training tasks where mainstream post-training algorithms fail to improve, BES enables consistent gains. At inference time, BES outperforms existing open-source frameworks on three open problem solving benchmarks in both average and best-case performance, and outperforms majority voting and Monte Carlo Tree Search on mathematical reasoning and competitive programming benchmarks. Code and trained models are available at https://github.com/Embodied-Minds-Lab/BES.
Abstract:Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely used methods such as best-of-N sampling and tree search face two fundamental limitations: they are guided by sparse verification signals, and they construct candidates primarily through autoregressive expansion, restricting exploration to regions with substantial model probability mass. To address these, we propose Bidirectional Evolutionary Search (BES), a search framework that couples forward candidate evolution with backward goal decomposition. In the forward search, BES augments standard expansion with evolution operators that recombine partial trajectories to generate candidates that are difficult to obtain from a single model rollout. In the backward search, BES recursively decomposes the original task into checkable sub-goals, producing dense intermediate feedback that guides forward search. We provide motivation illustrating that candidates generated by expansion-only search may be confined to a narrow entropy shell, while evolution operators may escape it. Experiments show that on challenging post-training tasks where mainstream post-training algorithms fail to improve, BES enables consistent gains. At inference time, BES outperforms existing open-source frameworks on three open problem solving benchmarks in both average and best-case performance, and outperforms majority voting and Monte Carlo Tree Search on mathematical reasoning and competitive programming benchmarks. Code and trained models are available at this https URL.
Computation and Language (cs.CL)
arXiv:2605.28814 [cs.CL]
(or arXiv:2605.28814v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28814 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 313 words
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