The authors built GRIDUMM, a synthetic environment with a shared model trunk, asymmetric understanding and generation objectives, autoregressive generation, and a roughly order-of-magnitude token-budget imbalance.
Gradient conflict fails to predict multimodal model trade-offs
In a controlled synthetic testbed, reducing conflict between understanding and generation gradients did not improve their eventual performance balance.
Academic
Shuyang Jiang · Fucheng Deng · Yuchuan Luo · Zhenyu Wu
University of California, Los Angeles · Aimakj · National University of Defense Technology · Key Laboratory of Advanced Microprocessor Chips and Systems
Research Digest··3 min read
Jiang et al.
Why this paper
From University of California, Los Angeles and 3 others
In one line
Gradient conflict metrics do not reliably predict the understanding-generation trade-off in multimodal models.
What we could check
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