LDDR proposes a linear DPP-based dynamic-resolution frame sampler that achieves 3x speedup and up to 2.5-point gains on video MLLM benchmarks by selecting non-redundant frames and allocating tokens accordingly.
Resadapt: Adaptive resolution for efficient multimodal reasoning
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EchoPrune prunes video tokens via query relevance and temporal reconstruction error to let VideoLLMs handle up to 20x more frames under fixed budget with reported gains in accuracy and speed.
PreRL applies reward-driven updates to P(y) in pre-train space, uses Negative Sample Reinforcement to prune bad reasoning paths and boost reflection, and combines with standard RL in Dual Space RL to outperform baselines on reasoning tasks.
citing papers explorer
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LDDR: Linear-DPP-Based Dynamic-Resolution Frame Sampling for Video MLLMs
LDDR proposes a linear DPP-based dynamic-resolution frame sampler that achieves 3x speedup and up to 2.5-point gains on video MLLM benchmarks by selecting non-redundant frames and allocating tokens accordingly.
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EchoPrune: Interpreting Redundancy as Temporal Echoes for Efficient VideoLLMs
EchoPrune prunes video tokens via query relevance and temporal reconstruction error to let VideoLLMs handle up to 20x more frames under fixed budget with reported gains in accuracy and speed.
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From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space
PreRL applies reward-driven updates to P(y) in pre-train space, uses Negative Sample Reinforcement to prune bad reasoning paths and boost reflection, and combines with standard RL in Dual Space RL to outperform baselines on reasoning tasks.