@inproceedings{yu-etal-2026-trajselector,
title = "{T}raj{S}elector: Harnessing Latent Representations for Effective and Robust Best-of-N Selection in Large Reasoning Models",
author = "Yu, Bin and
Wang, Xinming and
Lian, Shijie and
Li, Haotian and
Wu, Changti and
Hu, Ruina and
Wang, Bailing and
Wei, Yuliang and
Chen, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.214/",
pages = "4467--4484",
ISBN = "979-8-89176-462-0",
abstract = "Test-time scaling (TTS) has emerged as a powerful paradigm for enhancing large language model (LLM) reasoning by allocating additional compute during inference. External TTS, particularly Best-of- selection, generates multiple reasoning trajectories and selects the optimal candidate. However, existing process reward models (PRMs) for Best-of-$N$ selection face a critical limitation: they were not originally designed for this task and instead rely on human-designed evaluation criteria (e.g., readability, coherence, formatting) that often misalign with actual answer correctness, leading to unstable and oscillatory performance as $N$ increases. We introduce TrajSelector a framework specifically designed for robust and effective Best-of-$N$ selection. Our key insight is to leverage the sampler LLM{'}s intrinsic hidden states as self-reflective signals, which naturally align with correctness rather than human-imposed stylistic preferences. A lightweight verifier (0.6B{--}4B parameters) scores reasoning steps using these hidden states, trained via outcome supervision to eliminate reliance on step-level human annotations. Experiments across six mathematical reasoning benchmarks demonstrate that TrajSelector achieves robust monotonic improvements: in Best-of-32 settings, even our smallest 0.6B verifier outperforms majority voting by 4.61{\%} and existing 7B PRMs by 4.31{--}12.21{\%}, while our 4B verifier reaches 64.05{\%} accuracy with stable scaling behavior as $N$ increases from 1 to 64."
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<abstract>Test-time scaling (TTS) has emerged as a powerful paradigm for enhancing large language model (LLM) reasoning by allocating additional compute during inference. External TTS, particularly Best-of- selection, generates multiple reasoning trajectories and selects the optimal candidate. However, existing process reward models (PRMs) for Best-of-N selection face a critical limitation: they were not originally designed for this task and instead rely on human-designed evaluation criteria (e.g., readability, coherence, formatting) that often misalign with actual answer correctness, leading to unstable and oscillatory performance as N increases. We introduce TrajSelector a framework specifically designed for robust and effective Best-of-N selection. Our key insight is to leverage the sampler LLM’s intrinsic hidden states as self-reflective signals, which naturally align with correctness rather than human-imposed stylistic preferences. A lightweight verifier (0.6B–4B parameters) scores reasoning steps using these hidden states, trained via outcome supervision to eliminate reliance on step-level human annotations. Experiments across six mathematical reasoning benchmarks demonstrate that TrajSelector achieves robust monotonic improvements: in Best-of-32 settings, even our smallest 0.6B verifier outperforms majority voting by 4.61% and existing 7B PRMs by 4.31–12.21%, while our 4B verifier reaches 64.05% accuracy with stable scaling behavior as N increases from 1 to 64.</abstract>
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%0 Conference Proceedings
%T TrajSelector: Harnessing Latent Representations for Effective and Robust Best-of-N Selection in Large Reasoning Models
%A Yu, Bin
%A Wang, Xinming
%A Lian, Shijie
%A Li, Haotian
%A Wu, Changti
%A Hu, Ruina
%A Wang, Bailing
%A Wei, Yuliang
%A Chen, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yu-etal-2026-trajselector
%X Test-time scaling (TTS) has emerged as a powerful paradigm for enhancing large language model (LLM) reasoning by allocating additional compute during inference. External TTS, particularly Best-of- selection, generates multiple reasoning trajectories and selects the optimal candidate. However, existing process reward models (PRMs) for Best-of-N selection face a critical limitation: they were not originally designed for this task and instead rely on human-designed evaluation criteria (e.g., readability, coherence, formatting) that often misalign with actual answer correctness, leading to unstable and oscillatory performance as N increases. We introduce TrajSelector a framework specifically designed for robust and effective Best-of-N selection. Our key insight is to leverage the sampler LLM’s intrinsic hidden states as self-reflective signals, which naturally align with correctness rather than human-imposed stylistic preferences. A lightweight verifier (0.6B–4B parameters) scores reasoning steps using these hidden states, trained via outcome supervision to eliminate reliance on step-level human annotations. Experiments across six mathematical reasoning benchmarks demonstrate that TrajSelector achieves robust monotonic improvements: in Best-of-32 settings, even our smallest 0.6B verifier outperforms majority voting by 4.61% and existing 7B PRMs by 4.31–12.21%, while our 4B verifier reaches 64.05% accuracy with stable scaling behavior as N increases from 1 to 64.
%U https://aclanthology.org/2026.findings-emnlp.214/
%P 4467-4484
Markdown (Informal)
[TrajSelector: Harnessing Latent Representations for Effective and Robust Best-of-N Selection in Large Reasoning Models](https://aclanthology.org/2026.findings-emnlp.214/) (Yu et al., Findings 2026)
ACL
- Bin Yu, Xinming Wang, Shijie Lian, Haotian Li, Changti Wu, Ruina Hu, Bailing Wang, Yuliang Wei, and Kai Chen. 2026. TrajSelector: Harnessing Latent Representations for Effective and Robust Best-of-N Selection in Large Reasoning Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4467–4484, Budapest, Hungary. Association for Computational Linguistics.