Chart reasoning poses a significant challenge for Vision Large Language Models (VLLMs). While Reinforcement Learning (RL) has emerged as a promising solution, existing outcome-based RL methods overlook intermediate errors, whereas process-based approaches remain difficult in verifying abstract logical steps. We therefore analyze chart-reasoning failures to identify an intermediate supervision signal that is both relevant to the task and amenable to objective verification. Our pilot study suggests that early visual-perception errors constitute a prominent source of failure in the chart-reasoning setting. Guided by this insight, we propose Chart-EARL, which constructs verifiable early visual anchors for targeted chart-reasoning supervision. We first build a structured training corpus that explicitly decouples visual perception from logical deduction. Building upon this structure, our approach optimizes the model with rewards for both accurate extraction of descriptive visual features and final-answer correctness. This strategy provides direct and verifiable feedback on early visual grounding while keeping supervision on the final reasoning objective. Experiments show clear performance improvements on complex chart reasoning benchmarks.

Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art. This task is challenging as the audience often lacks specific knowledge about the presented research. We propose JRE-L, a framework that integrates three LLMs mimicking the writing-reading-feedback-revision loop. In JRE-L, one LLM acts as the journalist, another LLM as the general public reader, and the third LLM as an editor. The journalist’s writing is iteratively refined by feedback from the reader and suggestions from the editor. Our experiments demonstrate that by leveraging the collaboration of two 7B and one 1.8B open-source LLMs, we can generate articles that are more accessible than those generated by existing methods, including prompting single advanced models such as GPT-4 and other LLM-collaboration strategies. Our code is publicly available at github.com/Zzoay/JRE-L.