aiqbench analysis
This study identifies a fundamental bottleneck in pointwise reranking where Chain-of-Thought models struggle to resolve ranking signals when routing continuous relevance semantics through discrete text generation. Despite extensive stress testing with reinforcement learning and architectural modifications, the performance gap against direct scoring models remains stable even at 32B parameter scales. The findings suggest this limitation is intrinsic to the current paradigm rather than a resolvable training bias, offering critical insights for researchers optimizing retrieval systems.
Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking
arXiv:2608.30398v1 Announce Type: new Abstract: In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to inferior classification, score polarization, or calibration breakdown, whether targeted training can bridge this gap remains unclear. Our empirical study first confirms that this gap is stable across scales up to 32B parameters, ruling out model and data capacity confounders. We then apply stress tests utilizing reinforcement learning, fine-grained supervision, and architectural decoupling to explicitly repair these deviations. Although these interventions improve classification accuracy and absolute scores, the relative ranking gap persists. These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution, revealing a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.
Abstract:In pointwise document reranking, Chain-of-Thought models typically underperform direct scoring models. While existing diagnostics attribute this to inferior classification, score polarization, or calibration breakdown, whether targeted training can bridge this gap remains unclear. Our empirical study first confirms that this gap is stable across scales up to 32B parameters, ruling out model and data capacity confounders. We then apply stress tests utilizing reinforcement learning, fine-grained supervision, and architectural decoupling to explicitly repair these deviations. Although these interventions improve classification accuracy and absolute scores, the relative ranking gap persists. These findings suggest that, within the pointwise scoring paradigm, routing continuous relevance semantics through discrete text constrains ranking signal resolution, revealing a bottleneck that is stable and difficult to overcome under current standard methods, rather than an easily resolvable training bias.
Computation and Language (cs.CL); Information Retrieval (cs.IR)
arXiv:2608.30398 [cs.CL]
(or arXiv:2608.30398v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.30398 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 227 words
