Research2026-08-20
Researchers proposed Scaffolding Minds to improve latent visual reasoning. It uses a dedicated encoder for optimized latent targets. It also learns mean and variance for the RL sampler. The method addresses suboptimal latent representations in SFT. It also fixes limited exploration in existing RL methods. The two improvements are complementary. It outperforms the strongest baseline on spatial planning. Gains widen at larger grid sizes. It also beats baselines across nine visual reasoning tests.
What changed
Latent reasoning used off-the-shelf vision encoders and deterministic RL regularization.
What it unlocks
Creating alternative latent trajectories for exploration in multimodal reasoning.
- +9.5% on FrozenLake spatial planning
- +19% at 32x32 grid map
- +5.2% across nine visual-centric reasoning benchmarks
Sources