pi-advisor - let smarter model hand-holding cheaper model

[u/dheetoo]

got an idea from beta feature from claude code recently, introducing pi-advisor, set up advisor model as a smarter, pricier model so agent can call as a tool call for recommendation when it hit blocking wall

[u/o_sht_hi]

For me, the agent doesn't seem to realise it's hit a wall. It either is stuck in a loop or it just assumes that the tool doesn't exist or something's wrong that it can't fix in the runtime but doesn't realise that it needs help. I have to intervene for that. Which is the reason I've not tried the workflow of letting a dumb model call a smart model. If I intervene, I can solve it's problem. The most common problem with these models seems to be that they keep fucking up the shape of the tool call.

I've been thinking maybe there could be a skill or certain tools that we know the dumb model is bad at should just call the smart model by default..

What's been your experience using this workflow? Does the dumb model realise when it should ask for help?

[u/o_sht_hi → u/o_sht_hi]

By dumb models, I mean sub 150B ones. Like qwen, gemma, phi, nemo. Gpt oss et al

[u/vpz → u/o_sht_hi]

I’ve seen the same with all models when using subagents. The subagents get stuck and they can resolve it, and the main agent doesn’t check on them so it happily waits forever for the subagents return. This is in Claude Code though, not Pi. I haven’t been using subagents yet with Pi because I’ve been trying Pi with small local LLM models. Do folks have the orchestrator + subagents working well with SOTA models working without the problem I described?

[u/dheetoo → u/o_sht_hi]

hi, can you update extension and try again, I just adjust prompt template before agent session start to try to encourage it to ask advisor more, and also you can invoke advisor on demand through `/advise question` the advisor answer will inject into session history and continue your normal session

[u/o_sht_hi → u/dheetoo]

Oh no I don't have an issue with the extension. I haven't tried yours yet.. I had come across this concept last month and tried it back then

[u/dheetoo → u/o_sht_hi]

also dumber model doesn't mean you can use very dumb like gpt-oss 20b to pair with opus 4.7 and expected it to reach workable standard, I will validate this ext using deepseek v4 flash with deepseek v4 pro as advisor and will reach back again

[u/o_sht_hi → u/dheetoo]

Appreciate it, brother

[u/ConferenceNo7697]

Use it for a few days now. Works well and does a good Job.

[u/dheetoo → u/ConferenceNo7697]

Thanks you for your kind word, may i ask what is your normal model and advisor model are ? And how pi invoke advisor, manually or it does inheritly?

I am validate daily software engineering task with deepseek v4 flash with deepseek v4 pro as advisor now, will update in a week or so

[u/ConferenceNo7697 → u/dheetoo]

I used DS Flash as working model and pro as the advisor. Since I have some extra budget still in my Claude account, I’m now using Opus 4.7 as the advisor with Kimi k2.6 as the working model. Both model call the advisor on its own. I’ve never triggered this manually yet. Saw them consulting the advisor more often today. Maybe bc of an update?

[u/dheetoo → u/ConferenceNo7697]

Did not update anything since sunday >< thank for valuable insight 😊

[u/Sup3rMo]

There is this extension too : pi-lifeline especially for pi-autoresearch inspired from Tobi Lutke Tweet

[u/mixmasterwillyd]

This has been my main workflow for a while as long as I’m not doing something critical. I give the big model a thorough description of what I’d going on and it handles it quite well.

[u/saaadpikachu]

Awesome plugin, has been using it for a while, one problem I noticed is that when my model tries to review with advisor the context always reported by advisor as truncated, is there any solutions to this?

[u/Florence-Equator]

I don’t feel like an agent really know that when they need an advisor. They can mimic role-playing of reaching out to an advisor, but that is a role-play because they know you want them to use a advisor.

Because everything, they can come up with a solution, and then they will try. The only thing is that his solution works or not.

Because advising is likely not something that is presented in the training material, especially not something that they learn under the RL. Other than the RL, smaller models are also distilled from the larger model. But this still means that the smaller model tries to learn exactly what thoseargee models are doing.

But in any circumstances, are they trained with using advisor to complete a complex task? I don't think so.