SSL-R1 reformulates visual SSL tasks into verifiable puzzles to supply rewards for RL post-training of MLLMs, yielding gains on multimodal benchmarks without external supervision.
Momentum contrast for unsupervised visual rep- resentation learning
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.
citing papers explorer
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SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models
SSL-R1 reformulates visual SSL tasks into verifiable puzzles to supply rewards for RL post-training of MLLMs, yielding gains on multimodal benchmarks without external supervision.
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Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives
Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.