AgriSEED: Learning to Teach Agricultural Vision-Language Models

Generating useful supervision is a central challenge in adapting vision–language models to agricultural tasks. Although models can generate plausible question–answer pairs, answer validity alone does not indicate whether an example will help a solver learn. We introduce AGRISEED (Solver-Evaluated Example Design), a learning-to-teach framework that optimizes question generation using feedback from temporary solver adaptation. Given agricultural images, a questioner proposes question–answer pairs, and a frozen visual judge screens their quality. For each reliable candidate, a temporary solver undergoes a single low-rank adaptation step. Its learning gain is measured through changes in reference-answer likelihood on screened, self-generated probes from other images, complemented by augmented views of the candidate image. The temporary update is reset after evaluation, and the learning gain is combined with format and answer-quality rewards to train the questioner using GRPO. The trained questioner then generates a filtered dataset for a subsequent stage of solver reinforcement learning under the fixed visual judge. Experiments on AgroCoT with Qwen2.5-VL-7B, Qwen3-VL-8B, and InternVL2-8B show consistent improvements in semantic similarity and chainof-thought quality across all five agricultural task dimensions. A task-wise analysis shows consistent improvements in both semantic alignment and reasoning-step matching across the evaluated agricultural tasks and model backbones, supporting the effectiveness of AGRISEED for agricultural vision–language understanding.