The branch point is doing more work than the routing.

MOPD's teachers all branch from the same SFT checkpoint, so their token-level signal stays near the student's distribution. That constraint is the load-bearing part, and this week's feed has two papers on what breaks when you drop it.

DAPD (2608.01735) names it the privilege illusion: a teacher holding privileged context teaches behavior the student cannot reproduce from its own inference-time context, and the student then acts as if the privilege were still there. W2S-OPD (2607.26246) comes at it from the other side and builds its proxy teacher in logit space specifically to stay distributionally adjacent to the student, out of a contrast pair where both models are smaller than the student.

Three designs, same rule: closeness to the student's distribution buys more than the teacher's absolute strength.

Which makes drift the number I want from LFM2.5. How far can a domain teacher move away from that shared SFT checkpoint, through its own SFT plus RLVR, before MOPD starts costing you? Is there a point where you re-branch instead of pushing the teacher further?

The multi-turn harness part is the most interesting signal here.

A small model that can hold its objective inside changing tool state, partial context, and long-running agent loops may end up being more useful than a larger model that only looks strong on static prompts.

I’d love to see more reporting on long-run drift: when the agent starts optimizing for the harness instead of the task, when it forgets the original objective, and when it can recover after ambiguity or conflicting context.

That feels like the next important benchmark layer for agentic models: not just “can it solve the task once?”, but “does it remain coherent and useful after the environment starts moving around it?”

Yo convolution layers... You caused me to pay attention hahaha get it. An AI model would laugh at this joke and say "That joke is doing more work than the 22 conv layers, this is a pun on the causal convolution layers." Ahhh dad jokes aside I'm actually kinda stoked to run a little atlas on your model. It's like 22 people sitting at a bar eavesdropping on a few people adjacent to the left of them and predicting based on that narrow view point with 8 patrons listening to the entire thing with clarity and scratching their heads. Ima see what saving KV cache is doing here. To hell with the benchmarks and arXiv papers! Let's weigh it measure it and properly fit it in a suit. I wanna see if it looks properly proportioned or if it's a saw toothed oddball with wide shoulders a narrow midriff big ol' booty tiny calves huge shins and stiletto sized feet.