Attention is All You Need Until You Need Retention
Quick summary
arXiv:2501.09166v2 Announce Type: replace-cross Abstract: Pretrained Transformers keep what they learned in their weights and lose what they observe once a session ends. The first version of this paper proposed a Retention Layer, a persistent memory that a Transformer block reads with attention and writes during use. Because most of what a deployed model could retain is produced by other agents, this revision treats deciding what to keep as a social learning problem: when to rely on observed behaviour, whom to learn from and how much independent agreement to require. We give a corrected specif
Key takeaways
- arXiv:2501.09166v2 Announce Type: replace-cross Abstract: Pretrained Transformers keep what they learned in their weights and lose what they observe once a session ends.
- The first version of this paper proposed a Retention Layer, a persistent memory that a Transformer block reads with attention and writes during use.
- Because most of what a deployed model could retain is produced by other agents, this revision treats deciding what to keep as a social learning problem: when to rely on observed behaviour, whom to learn from and how much independent agreement to require.
Why it matters
“Attention is All You Need Until You Need Retention” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
Member comments