Two Simple Changes Nearly Double the Correlation of MLLM Encoder Scoring

The correlation between pre-evaluation scores for vision encoders and their real quality in multimodal LLMs has nearly doubled. That is the headline claim of a Jiqizhixin article on new work in which the authors propose two simple improvements to the selection method. The goal is practical: to know in advance which encoder will perform best, without training the entire stack for each one.

Why choosing an encoder is so expensive

A vision encoder turns an image into representations that the language model then works with. It is one of the key components of an MLLM. Take the same LLM, change only the encoder, and the final results on downstream tasks can differ substantially. The choice carries a weight comparable to the choice of the language backbone itself.

The direct way to find the best option is obvious and costly. Each candidate has to be connected to the LLM, run through cross-modal alignment, and then evaluated on benchmarks. With a dozen candidates that differ in architecture, pretraining data, and resolution, the bill runs to significant GPU-hours. So the industry needs a cheap proxy: a test that runs before full training and ranks encoders reliably.

What is known about the new method

The problem with proxy scores is that they often match the final ranking poorly. A metric that does not predict final quality is pointless to use. The quality of such a proxy is usually measured by rank correlation between the predicted and actual order of encoders. According to the outlet, this is the figure the authors nearly doubled.

The available excerpt of the article does not disclose what the two improvements actually are, which encoders and benchmarks they were tested on, or what absolute correlation values resulted. The "before" and "after" numbers are unknown, so "nearly double" should be read as the authors' claim, not an independently verified result. The size of the gain depends heavily on the starting point: a rise in correlation from 0.3 to 0.55 and one from 0.5 to 0.9 are very different stories.

What can be established now:

• Subject: pre-evaluation of vision encoders for MLLMs without a full training cycle.
• Problem: full alignment and evaluation of each candidate require heavy compute.
• Result: the correlation between the score and final performance nearly doubled.
• Approach: two "simple" improvements, meaning no costly overhaul of the pipeline.
• Not disclosed: the set of encoders, the benchmarks, the absolute correlation values, the cost of the evaluation itself.

How to apply the result in practice

For teams building their own multimodal models, the value of this work lies in saving budget in the early stages. If the proxy score really does rank candidates more accurately, a dozen encoders can be cut to two or three before full training. The freed resources can go toward longer alignment or toward data.

Before adopting it, check three things. First, reproducibility on your own LLM family, because a result obtained on one language backbone does not necessarily carry over to another. Second, correlation on the tasks that matter to you, whether OCR, document processing, or visual reasoning. A general benchmark can hide a failure on a narrow metric. Third, how much compute the pre-evaluation itself takes. If it is comparable to a short full training run, the gain disappears.

Until the full text of the study has been examined, the sensible position is this: the direction is practical and the claimed gain is notable, but the decision to adopt it should wait until it has been tested on your own encoder-plus-LLM combinations.