MAPLE: Modality-Aware Post-training and Learning Ecosystem | ScienceToStartup | ScienceToStartup

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References (37)

[1]

GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

2026Shih-Yang Liu, Xin Dong et al.

[2]

Learning What to Attend First: Modality-Importance-Guided Reasoning for Reliable Multimodal Emotion Understanding

2025Hyeongseop Rha, Jeong Hun Yeo et al.

[3]

The Art of Scaling Reinforcement Learning Compute for LLMs

Founder's Pitch

"MAPLE offers a modality-aware ecosystem to enhance post-training in multimodal RL by optimizing signal-specific learning strategies for faster convergence and improved robustness."

Multimodal RL•Score: 7•View PDF ↗

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MAPLE: Modality-Aware Post-training and Learning Ecosystem

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References (37)

Founder's Pitch

"MAPLE offers a modality-aware ecosystem to enhance post-training in multimodal RL by optimizing signal-specific learning strategies for faster convergence and improved robustness."

Commercial Viability Breakdown

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Why It Matters

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MAPLE: Modality-Aware Post-training and Learning Ecosystem

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References (37)

Founder's Pitch

"MAPLE offers a modality-aware ecosystem to enhance post-training in multimodal RL by optimizing signal-specific learning strategies for faster convergence and improved robustness."

Commercial Viability Breakdown

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