1 citations · 1 across the 9 of their papers we have counts for
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Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves…
PRISM: Perception Reasoning Interleaved for Sequential Decision Making
Mohamed Salim Aissi, Clemence Grislain, Clement Romac +4
Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remains a major challenge. Recent work identifies a perception-reasoning-decision gap i…
MODIP: Efficient Model-Based Optimization for Diffusion Policies
Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome +1
Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However,…
Latent Goal Prediction from Language for Model-Based Planning
Samuel Barbeau, Simon Roy, Giovanni Beltrame +2
Joint-Embedding Predictive Architectures (JEPAs) enable agents to plan in latent space by imagining the outcomes of candidate actions, yet task specification remains a bottleneck.…
V-GIFT: Boosting Visual Instruction Tuning with Self-Supervised Guidance
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual perception. Rec…