ResMerge improves merging of RL expert LLMs via a stable residual consensus backbone plus gated head correction, outperforming task-vector and spectral baselines in capability preservation.
arXiv preprint arXiv:2502.04959 , year=
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PACT preserves load-bearing wall dimensions from pre-trained weights inside task vectors to reduce conflicts and improve merged model performance.
PivotMerge merges post-alignment cross-modal projectors from heterogeneous MLLM pre-training via shared-space filtering and layer-wise weights, beating prior merging baselines on multimodal benchmarks.
Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.
citing papers explorer
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ResMerge: Residual-based Spectral Merging of Large Language Models
ResMerge improves merging of RL expert LLMs via a stable residual consensus backbone plus gated head correction, outperforming task-vector and spectral baselines in capability preservation.
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PACT: Preserving Anchored Cores in Task-vectors for Model Merging
PACT preserves load-bearing wall dimensions from pre-trained weights inside task vectors to reduce conflicts and improve merged model performance.
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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
PivotMerge merges post-alignment cross-modal projectors from heterogeneous MLLM pre-training via shared-space filtering and layer-wise weights, beating prior merging baselines on multimodal benchmarks.
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Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.