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.
Ssl4rl: Revisiting self-supervised learning as intrinsic reward for visual-language reasoning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Mixing 3-10% of visually grounded self-supervised instructions into visual instruction tuning consistently boosts MLLM performance on vision-centric benchmarks.
VGPO introduces visual attention compensation and dual-grained advantage re-weighting to reinforce visual focus in VLMs, yielding better activation and performance on multimodal reasoning tasks.
citing papers explorer
-
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.
-
Boosting Visual Instruction Tuning with Self-Supervised Guidance
Mixing 3-10% of visually grounded self-supervised instructions into visual instruction tuning consistently boosts MLLM performance on vision-centric benchmarks.
-
Visually-Guided Policy Optimization for Multimodal Reasoning
VGPO introduces visual attention compensation and dual-grained advantage re-weighting to reinforce visual focus in VLMs, yielding better activation and performance on multimodal reasoning tasks.