Recent reasoning models through test-time scaling have demonstrated that long
chain-of-thoughts can unlock substantial performance boosts in hard reasoning
tasks such as math and code. Cependant, the benefit of such long thoughts for
system-2 reasoning is relatively less explored in other domains such as
perceptual tasks where shallower, system-1 reasoning seems sufficient. In this
paper, we introduce LongPerceptualThoughts, a new synthetic dataset with 30K
long-thought traces for perceptual tasks. The key challenges in synthesizing
elaborate reasoning thoughts for perceptual tasks are that off-the-shelf models
are not yet equipped with such thinking behavior and that it is not
straightforward to build a reliable process verifier for perceptual tasks.
Thus, we propose a novel three-stage data synthesis framework that first
synthesizes verifiable multiple-choice questions from dense image descriptions,
then extracts simple CoTs from VLMs for those verifiable problems, and finally
expands those simple thoughts to elaborate long thoughts via frontier reasoning
models. In controlled experiments with a strong instruction-tuned 7B model, we
demonstrate notable improvements over existing visual reasoning data-generation
methods. Our model, trained on the generated dataset, achieves an average +3.4
points improvement over 5 vision-centric benchmarks, including +11.8 points on
V$^*$ Bench. Notably, despite being tuned for vision tasks, it also improves
performance on the text reasoning benchmark, MMLU-Pro, by +2 points.

Cet article explore les excursions dans le temps et leurs implications.

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