aiqbench analysis
RoVE introduces a parameter-free modification to Rotary Position Embeddings that renders the value pathway position-sensitive, effectively transforming RoPE attention into an attentive convolution mechanism. Empirical evaluations on GPT-2 models demonstrate consistent improvements in few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval, particularly for tasks requiring long-range aggregation. This approach unifies existing formulations across computer vision and robotics while offering a robust upgrade to standard attention mechanisms without additional trainable parameters.
RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways
arXiv:2606.11275v1 Announce Type: new Abstract: Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value token is the same regardless of its distance from the query. We propose RoVE, a parameter-free modification that makes values position-sensitive by rotating them simultaneously with keys, and show that it turns RoPE attention into attentive convolution. This new perspective unifies several independent formulations of the same operation across computer vision, robotics, and modern LLM architectures. Trained 124M and 354M GPT-2 models show consistent empirical gains over RoPE on few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval, with the clearest improvements on tasks that require long-range aggregation.
Abstract:Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value token is the same regardless of its distance from the query. We propose RoVE, a parameter-free modification that makes values position-sensitive by rotating them simultaneously with keys, and show that it turns RoPE attention into attentive convolution. This new perspective unifies several independent formulations of the same operation across computer vision, robotics, and modern LLM architectures. Trained 124M and 354M GPT-2 models show consistent empirical gains over RoPE on few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval, with the clearest improvements on tasks that require long-range aggregation.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.11275 [cs.LG]
(or arXiv:2606.11275v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11275 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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