New Reinforcement Learning Method Improves Tool-Use Capabilities in Small Multimodal AI Models
Researchers have proposed Input Attribution-Aware Policy Optimization (IAPO), a reinforcement learning algorithm designed to improve how small multimodal language models select and use external tools. The method addresses limitations of existing approaches that rely on exact-match rewards and predefined formats, which are poorly suited to multimodal tasks where multiple valid tool-use paths may exist. Experiments on Qwen2.5-VL-3B show a 3% average improvement in visual question answering accuracy across six test sets.
A paper submitted to arXiv proposes IAPO, a reinforcement learning algorithm aimed at enhancing tool-calling capabilities in small multimodal language model (SLM) agents. Current methods for training such agents typically evaluate tool use through exact matching against ground-truth or predefined formats, a strategy the authors argue is ill-suited to multimodal scenarios where annotated tool trajectories are often unavailable and multiple valid tool-use paths may exist. Additionally, the sparse and binary nature of such rewards offers limited learning signal for small models. IAPO addresses these shortcomings by aligning the model's input attribution—how it weights different components of its input—with that of a stronger teacher model, providing richer guidance during training. Tested on the Qwen2.5-VL-3B model, IAPO achieved an average 3% improvement in visual question answering accuracy across six benchmark test sets compared to existing visual tool-use methods. The authors attribute the gains to the model learning to attend more effectively to the most relevant input evidence when deciding which tools to invoke.
What's missing
The paper does not detail the specific six test sets used for evaluation, the baseline methods against which IAPO is compared, or whether the 3% improvement is statistically significant. It is also unclear how IAPO scales to larger models or whether the teacher-alignment approach introduces additional computational overhead at training time. The work has not yet undergone peer review.
What different sources said
- arXiv cs.LGCenter
IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents
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