Strata answer_room reportedly caps thinking and inserts wrap-up text
A r/LocalLLaMA user reports that Strata and PolyStrata enable answer_room by default when thinking is on, max_tokens is at least 1024, and no reasoning budget is specified. The reported behavior limits thinking to 0.75 times max_tokens, inserts a model-voiced wrap-up line, and continues the same response. The client receives no error, flag, or finish reason, and there is often no log. The user cited source-code locations, but the wider effects and whether the injected text helps or harms remain unverified.
According to the Reddit report, the reported cap and injected inner-voice text can change reasoning length and transcripts without an explicit client-side signal, complicating comparisons between Strata and other inference engines.
With default settings and thinking on and max_tokens ≥ 1024, and no explicit reasoning_budget_tokens, Strata / PolyStrata has a behavior called answer_room which will silently cap thinking at 0.75. * max_tokens and inject a "wrap-up" line in the model's head in its own voice, saying
I have thought about this long enough; time to give my answer. + </think>
It then continues the same reply from there. The client (harness, you or I) gets no error, flag or finish reason for this, and often no log.
This matters when you compare between strata and other engines : models served by strata had "nudges" in the form of its inner voice, and such extra text in the transcript. In some cases the nudge line is injected mid thinking, quite abruptly and it is hard to tell whether it helps or harms.
Strata main @ c62d5e3
Wrap-up message: https://github.com/Niko1221/Strata/blob/c62d5e3a15b40ea51c96aae9e8d36928965f53ab/serve/server.py#L225
Trigger (the 3/4 budget): https://github.com/Niko1221/Strata/blob/c62d5e3a15b40ea51c96aae9e8d36928965f53ab/serve/server.py#L5491
Injection point: https://github.com/Niko1221/Strata/blob/c62d5e3a15b40ea51c96aae9e8d36928965f53ab/serve/server.py#L5848
Default on (answer_room = True): https://github.com/Niko1221/Strata/blob/c62d5e3a15b40ea51c96aae9e8d36928965f53ab/serve/server.py#L4181
I noticed this when a model that tends to have long front-loaded thinking on dgx spark gave slightly but uniformly shorter answers (and task performance worsened in my hard problems), when served on polystrata.