Tiny Inference-Time Scaling with Latent Verifiers

📰 ArXiv cs.AI

Tiny Inference-Time Scaling with Latent Verifiers improves generative models using latent verifiers, reducing inference-time cost

advanced Published 25 Mar 2026

Action Steps

Employ latent verifiers in autoencoder latent space to reduce computation
Use Multimodal Large Language Models (MLLMs) as verifiers to improve performance
Optimize inference-time scaling to minimize costs and maximize efficiency
Implement diffusion pipelines to operate in latent space and reduce decoding requirements

Who Needs to Know This

AI engineers and ML researchers can benefit from this approach to optimize generative models and improve performance, while reducing computational costs

Key Insight

💡 Using latent verifiers can reduce inference-time cost while improving generative model performance

Key Takeaways

Tiny Inference-Time Scaling with Latent Verifiers improves generative models using latent verifiers, reducing inference-time cost

Full Article

Title: Tiny Inference-Time Scaling with Latent Verifiers

Abstract:
arXiv:2603.22492v1 Announce Type: cross 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 c

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