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[2602.21215] Inference-time Alignment via Sparse Junction Steering
Summary
This paper presents Sparse Inference-time Alignment (SIA), a novel approach to enhance alignment in large language models by intervening only at critical decision points, reducing computational costs while maintaining output quality.
Why It Matters
As large language models become integral to various applications, optimizing their performance without excessive computational demands is crucial. This research introduces a method that balances efficiency and effectiveness, potentially influencing future model training and deployment strategies.
Key Takeaways
- Sparse junction steering can achieve better alignment efficiency by targeting only critical decision points.
- Intervening on 20% to 80% of tokens can match or exceed the performance of heavily post-trained models.
- This method significantly reduces computational costs, offering up to 6x savings.
- High entropy junctions are identified as key areas for intervention to enhance model alignment.
- SIA integrates well with existing search-based methods, improving overall model performance.
Computer Science > Computation and Language arXiv:2602.21215 (cs) [Submitted on 30 Jan 2026] Title:Inference-time Alignment via Sparse Junction Steering Authors:Runyi Hu, Jie Zhang, Shiqian Zhao, Jiale Meng, Jiwei Li, Jason Zeng, Ming Wu, Michael Heinrich, Yonggang Wen, Tianwei Zhang View a PDF of the paper titled Inference-time Alignment via Sparse Junction Steering, by Runyi Hu and 9 other authors 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 objectives show that steering ...