• Computer Science > Computation and Language [Submitted on 30 Jan 2026] Title:Inference-time Alignment via Sparse Junction Steering View PDF HTML (experimental)Abstract:Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine grained control over large language models by modulating their output distributions without parameter updates • While effective, existing methods rely on dense intervention at every decoding step • This persistent manipulation not only incurs substantial computational overhead but also risks compromising generation quality by excessively drifting from the model’s intrinsic distribution • In this work, we show that dense intervention is unnecessary and propose Sparse Inference time Alignment (SIA), which performs sparse junction steering by intervening only at critical decision points along the generation trajectory • Our key insight is that high entropy junctions mark pivotal decision points in the generation trajectory and are particularly susceptible to misalignment, indicating the need to introduce alignment related reward signals at these points • Extensive experiments across different model families and alignment object

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  • Computer Science > Computation and Language [Submitted on 30 Jan 2026] Title:Inference-time Alignment via Sparse Junction Steering View PDF HTML (experimental)Abstract:Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine grained control over large language models by modulating their output distributions without parameter updates. While effective, existing methods rely on dense intervention at every decoding step. This persistent manipulation not only incurs substantial computational overhead but also risks compromising generation quality by excessi

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