aiqbench analysis
This arXiv paper is about AI, specifically language models, so it passes the topic gate. It gives a theoretical account of why masked diffusion language models resist the reversal curse better than autoregressive models, locating the cause in shared Transformer parameters that store token-pair associations, rather than in the any-order training objective alone. The claims are backed by a proof in a one-layer model and experiments on LLaDA and Dream, though the large-scale evidence is the part readers should check most closely against the full paper. For researchers working on diffusion LMs or on knowledge generalization in LLMs, the paper offers a mechanistic framing worth testing.
A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
arXiv:2602.02133v3 Announce Type: replace Abstract: Autoregressive language models (ARMs) suffer from the reversal curse: after learning ''$A$ is $B$,'' they often fail on the reverse query ''$B$ is $A$.'' Masked diffusion language models (MDMs) exhibit this failure in a much weaker form, but the underlying reason has remained unclear. A common explanation attributes this mitigation to their any-order masked training objective. However, observing ''$[\mathbf{M}]$ is $B$'' during training teaches recovery of $A$ from $B$ in one positional configuration, and does not by itself explain why the learned evidence should transfer to the reverse prompt ''$B$ is $[\mathbf{M}]$.'' We provide a theoretical analysis showing that this transfer arises from a parameter-level coupling between forward and reverse positional conditionals: shared Transformer parameters store token-pair evidence, while relative positional encodings route attention through queries and keys without changing the value-side evidence being retrieved. In a one-layer MDM, we prove that forward masked training strengthens evidence that is reusable in reverse queries, induces correlated forward--reverse attention routes, and yields a positively aligned shared-storage gradient component that decreases the reverse loss to first order. Controlled one-layer experiments and large-scale LLaDA/Dream experiments verify these signatures and show that they translate into improved reverse prediction.
Abstract:Autoregressive language models (ARMs) suffer from the reversal curse: after learning ''$A$ is $B$,'' they often fail on the reverse query ''$B$ is $A$.'' Masked diffusion language models (MDMs) exhibit this failure in a much weaker form, but the underlying reason has remained unclear. A common explanation attributes this mitigation to their any-order masked training objective. However, observing ''$[\mathbf{M}]$ is $B$'' during training teaches recovery of $A$ from $B$ in one positional configuration, and does not by itself explain why the learned evidence should transfer to the reverse prompt ''$B$ is $[\mathbf{M}]$.'' We provide a theoretical analysis showing that this transfer arises from a parameter-level coupling between forward and reverse positional conditionals: shared Transformer parameters store token-pair evidence, while relative positional encodings route attention through queries and keys without changing the value-side evidence being retrieved. In a one-layer MDM, we prove that forward masked training strengthens evidence that is reusable in reverse queries, induces correlated forward--reverse attention routes, and yields a positively aligned shared-storage gradient component that decreases the reverse loss to first order. Controlled one-layer experiments and large-scale LLaDA/Dream experiments verify these signatures and show that they translate into improved reverse prediction.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2602.02133 [cs.AI]
(or arXiv:2602.02133v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2602.02133 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 289 words
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