AI BriefingWhy dLLMs Are Prone to Collapse Under RL
·2026.04.16 09:00
Key point
This piece examines why dLLMs are prone to collapse when RL is applied to them.
Details
It addresses the concern that when a dLLM is optimized with RL, the model tends to show collapse as its output distribution skews to one side, rather than improving stably.
The core focus is on explaining what structural limitations dLLM-family models reveal in an RL setting, and as the title suggests, the emphasis is on analyzing the cause—"why does it collapse."
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