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This paper introduces a benchmark to validate that distribution-driven methods for activation steering in LLMs recover human value topologies aligned with Schwartz's theory, improving with model size but declining post-instruction tuning.
Introduces Bayesian Manifold Curriculum (BMC), an adaptive curriculum learning method for LLMs that leverages the model's latent geometry to allocate training effort across diverse problem types, improving efficiency beyond traditional difficulty-based curricula.
This paper proposes aligning latent geometry for spherical flow matching, projecting latents onto a fixed-radius sphere and using spherical linear interpolation to improve image generation quality, consistently improving FID on class-conditional ImageNet.