Tiny Inference-Time Scaling with Latent Verifiers

• URL: http://arxiv.org/abs/2603.22492v2
• Date: Wed, 25 Mar 2026 08:40:14 GMT
• Title: Tiny Inference-Time Scaling with Latent Verifiers
• Abstract summary: Verifier on Hidden States ( VHS) operates on intermediate hidden representations of Diffusion Transformer (DiT) single-step generators.<n> VHS analyzes generator features without decoding to pixel space, thereby reducing the per-candidate verification cost.<n> VHS achieves a +2.7% improvement on GenEval at the same inference-time budget.
• Score: 56.696619768584675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimodal Large Language Models (MLLMs) as verifiers, which can improve performance but introduce substantial inference-time cost. Indeed, diffusion pipelines operate in an autoencoder latent space to reduce computation, yet MLLM verifiers still require decoding candidates to pixel space and re-encoding them into the visual embedding space, leading to redundant and costly operations. In this work, we propose Verifier on Hidden States (VHS), a verifier that operates directly on intermediate hidden representations of Diffusion Transformer (DiT) single-step generators. VHS analyzes generator features without decoding to pixel space, thereby reducing the per-candidate verification cost while improving or matching the performance of MLLM-based competitors. We show that, under tiny inference budgets with only a small number of candidates per prompt, VHS enables more efficient inference-time scaling reducing joint generation-and-verification time by 63.3%, compute FLOPs by 51% and VRAM usage by 14.5% with respect to a standard MLLM verifier, achieving a +2.7% improvement on GenEval at the same inference-time budget.

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