Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning
Merged summary
TL;DR - Trace is a reproducible environment for training vision-language models with verifiable rewards across 11 visual domains. Training on 64,000 generated instances improved two Qwen2.5-VL models by 3.51–4.06 percentage points across 24 external benchmarks.
- Separates visual rendering from answer computation using scene grammars and executable task programs.
- Uses shared semantic state to generate images, prompts, typed answers, verifier states, and replayable traces.
- Includes 1,000 tasks spanning 277 scene grammars with controlled semantic and visual variation.
- Results suggest broad procedural RLVR training can transfer beyond its generated task distributions.
Sources (1)
Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning
TL;DR - Trace is a reproducible environment for training vision-language models with verifiable rewards across 11 visual domains. Training on 64,000 generated instances improved two Qwen2.5-VL models by 3.51–4.06 percentage points across 24 external benchmarks.
- Separates visual rendering from answer computation using scene grammars and executable task programs.
- Uses shared semantic state to generate images, prompts, typed answers, verifier states, and replayable traces.
- Includes 1,000 tasks spanning 277 scene grammars with controlled semantic and visual variation.
- Results suggest broad procedural RLVR training can transfer beyond its generated task distributions.