To separate hype from reality, we spent 3 months working firsthand with Anthropic's internal research model to see if it could create an experimental scorpion antivenom.

Introducing Citrini Therapeutics.

Reading MiMo-V2.6's write-up on tool-call repetition:

https://mimo.xiaomi.com/blog/mimo-v2-6-tool-call-repetition

Their fix is to collect repetition-prone contexts, train a specialist with single-turn RL + KL (positive reward only for no repetition AND no erroneous tool use), then integrate it into the main model through teacher-prefix MOPD. They report that the specialist already reaches zero replay repetition on their training and held-out sets after 12 steps (~7,000 examples).

What motivates the extra distillation stage? Why not continue RL directly on the mixed-RL checkpoint using those same contexts, reward and single-turn setup, then evaluate that repaired checkpoint? General-data replay or a retention objective could be added if other capabilities regress.

I mean a short, targeted repair run. The blog's estimated $2.31M full-MixRL alternative follows an earlier experiment with a stricter tool-call-count cap on a general data source; that does not seem to be a matched comparison with targeted RL on repetition cases.

I also realize the mixed-RL checkpoint and final MOPD model are different starting points. A useful control would be targeted RL starting from the integrated main model, compared against specialist RL + MOPD with initialization/stage effects accounted for.

Has anyone seen an ablation comparing repetition, downstream task performance and compute under matched repair data/reward? Does OPD provide a measurable retention/generalization benefit here, or is its main advantage fitting the existing multi-teacher pipeline? Would appreciate pointers or experience with similar behavioral repairs.

English translation/polishing assisted by an LLM.