Mechanistic Analysis of Alignment Algorithms in Language Models

• URL: http://arxiv.org/abs/2606.09850v1
• Date: Sat, 09 May 2026 06:36:06 GMT
• Title: Mechanistic Analysis of Alignment Algorithms in Language Models
• Abstract summary: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations.<n>We present a systematic analysis of six preference-optimization methods across three open-weight model families.<n>We find that preference signals consistently concentrate in early--mid or mid--late layers, but different objectives induce distinct representational shifts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We present a systematic mechanistic analysis of six preference-optimization methods: PPO, DPO, SimPO, ORPO, GRPO, and KTO across three open-weight model families. By integrating layer-wise linear probing, Sparse Autoencoders, and crosscoders, we localize preference representations and quantify alignment-induced geometric transformations in latent space. We find that preference signals consistently concentrate in early--mid or mid--late layers, but different objectives induce qualitatively distinct representational shifts. KTO and GRPO enhance linear separability through constructive feature sharing and sparse, high-salience recruitment. In contrast, DPO and ORPO degrade separability via non-constructive geometric rotation and feature attenuation, while PPO and SimPO largely preserve baseline geometry. These transformations exhibit architecture-dependent variability, demonstrating that behavioral alignment does not imply uniform internal restructuring. Our findings establish alignment as a heterogeneous intervention, motivate standardized feature-level auditing for safety and interpretability, and highlight the need for mechanism-aware optimization objectives.

Related papers

• Learning to Tessellate: Point Cloud Generation via Recursive Spectral Partitioning [58.130498928480456]
  We present PointRSP, an autoregressive framework that reformulates point cloud generation as a topology-preserving tessellation process.<n>PointRSP achieves state-of-the-art performance in generation quality and diversity, demonstrating strong generalization across complex 3D topologies.
  arXiv  Detail & Related papers  (2026-08-03T16:11:43Z)
• Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency [53.28076739140119]
  We propose a framework that enforces logical consistency in Vision-Language Models (VLMs) through geometric and linguistic duality operations.<n>SAGE is model-agnostic, data-efficient compared to prior GRPO methods, and can be applied as a lightweight post-training stage to any existing VLM.<n> Experiments on video and spatial reasoning benchmarks demonstrate consistent improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-05-18T10:05:21Z)
• Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers [93.3976834364707]
  We propose sREPA, a structural REPresentation Alignment framework to enforce consistency in the relational geometry of feature maps.<n>By encouraging the model to internalize holistic spatial layouts and structural correlations from pre-trained features, sREPA achieves faster and more stable convergence.
  arXiv  Detail & Related papers  (2026-05-16T12:01:04Z)
• Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning [52.452902154360565]
  GTSA-PCA is a geometric extension of Principal Component Analysis.<n>It integrates curvature awareness and geodesic consistency within a unified spectral framework.<n>Our results position GTSA-PCA as a principled bridge between statistical and geometric approaches to dimensionality reduction.
  arXiv  Detail & Related papers  (2026-04-20T20:36:36Z)
• From Directions to Regions: Decomposing Activations in Language Models via Local Geometry [37.50120706345745]
  We leverage Mixture of Factor Analyzers (MFA) as a scalable, unsupervised alternative that models the activation space.<n>MFA decomposes activations into two compositional geometric objects: the region's centroid in activation space, and the local variation from the centroid.<n>We train large-scale MFAs for Llama-3.1-8B and Gemma-2-2B, and show they capture complex, nonlinear structures in activation space.
  arXiv  Detail & Related papers  (2026-02-02T18:49:05Z)
• Orthogonalized Policy Optimization:Decoupling Sampling Geometry from Optimization Geometry in RLHF [0.0]
  Large language model alignment objectives are often presented as a collection of distinct algorithms, such as PPO, DPO, IPO, and their variants.<n>In this work, we argue that this diversity obscures a simpler underlying structure.<n>We show that this entanglement is not merely a modeling convenience but a source of systematic instability.
  arXiv  Detail & Related papers  (2026-01-18T13:57:44Z)
• Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency [7.889121135601528]
  Current unsupervised domain adaptation methods rely on fine-tuning feature extractors.<n>We propose Feature-space Planes Searcher (FPS) as a novel domain adaptation framework.<n>We show that FPS achieves competitive or superior performance to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-26T05:39:21Z)
• Structural DID with ML: Theory, Simulation, and a Roadmap for Applied Research [3.0031348283981987]
  Causal inference in observational panel data has become a central concern in economics,policy analysis, and the broader social sciences.<n>This paper proposes an innovative framework called S-DID that integrates structural identification with high-dimensional estimation.
  arXiv  Detail & Related papers  (2025-07-21T03:57:42Z)
• PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation [70.98107766265636]
  This paper takes the geometric attributes of pre-trained weights as a starting point, systematically analyzing three key components: magnitude, absolute angle, and pairwise angular structure.<n>We find that the pairwise angular structure remains stable across diverse corrupted domains and encodes domain-invariant semantic information, suggesting it should be preserved during adaptation.
  arXiv  Detail & Related papers  (2025-06-03T05:18:15Z)
• Target-Embedding Autoencoders for Supervised Representation Learning [111.07204912245841]
  This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of target-embedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets.
  arXiv  Detail & Related papers  (2020-01-23T02:37:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.