ResearchPod Summary
As multimodal large language models (MLLMs) are increasingly trained using reinforcement learning (RL), the quality of the reward signal becomes a primary bottleneck. Existing pipelines often rely on unverified data or assume that the provided answers are correct, which can lead to the systematic reinforcement of incorrect reasoning. This paper asks: how can we scale the construction of high-quality, image-grounded mathematical data while ensuring that both the prompts are challenging and the answers are objectively verifiable?
The authors introduce VeriEvol, an iterative framework that separates data construction into two independent axes: prompt difficulty and answer reliability.
VeriEvol demonstrates that scaling data volume while enforcing strict verification leads to consistent performance gains. Scaling SFT data from 10K to 250K samples increased mean accuracy from 35.42% to 54.73% on a five-benchmark suite. Furthermore, when holding the model backbone and RL recipe constant, VeriEvol provided a cumulative +3.88 accuracy boost over an un-evolved baseline, with roughly equal contributions coming from the evolved prompts (+1.82) and the HTV-Agent verification (+2.06).
This work shifts the paradigm of data construction from "trusting the labeller" to "verifiable data construction." By releasing the full verifier trace for every sample, the authors enable researchers to audit the pipeline's decision-making process. This modular approach allows for the addition of new verifier channels or evolution routes without needing to re-engineer the underlying RL policy optimization, providing a scalable path for improving reasoning in multimodal models.
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