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About this paper
Md Tanvirul Alam
TRACE, a taxonomy-guided environment, generates broad, verifiable, and reproducible training data for vision-language models (VLMs) to enhance reasoning via Reinforcement Learning with Verifiable Rewards (RLVR). Training VLMs on TRACE instances led to improved performance of 3.51 to 4.06 percentage points on 24 external visual-reasoning benchmarks, outperforming other synthetic-data RLVR checkpoints.
Key insights
- A structured, procedural approach to data generation, which separates visual elements from computational logic, enables the creation of highly controllable and exactly verifiable visual reasoning tasks.
- Training VLMs on broadly diverse but procedurally generated and verifiable data can lead to transferable reasoning capabilities that generalize to a wide array of external, unseen benchmarks.
- By making task definitions, supervision, and reward computation explicit, TRACE supports more reproducible multimodal-reasoning research and reduces reliance on proprietary or sensitive real-world imagery for specific contexts.
Summary by alphaXiv.
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