skull8888888

oh yeah, we did small-ish sft run to teach the model our trace compression style and so on. the problem with it is that you can't really teach good analysis this way, so RL was the core focus.

yes! we spend a lot of time on training to do exactly this.

hm, did the cost of tickets processed by an agent go down tho?

>Curious how you decide which flagged errors matter most

flow-1 actually predicts how critical each finding is

this is a very valid point. I will expand on RL post train here.

I used GSPO because it worked really well for very long prompts (we're dealing with traces analyses so prompts are very very long, and by prompts I mean what goes into LLM call).

We RL-ed flow-1 in our harness, which made it very efficient at using tools it had. Our harness as I said in the post is basically coding agent, it has grep, read and couple of other aux tools. I spent a loooot of time and money on figuring out the right RL recipe and especially the reward. It was really hard to find the right balance between exploration and exploitation.

Some RL runs are still running, because the prod checkpoint as good as it is, thinks a lot. It produces a lot of thinking tokens, which affects the throughput, especially on the large, complex traces to be analyzed. So the next big challenge for me is to make it efficient thinker - i.e. reduce thinking tokens, without losing quality.

I can write some bullet points if you want. I'm serious, this is not a sarcasm. With which AO platform do you want a comparison?

>for creating traces from many test calls to speed up testing of voice bots

I assume you mean tracing voice agents in general. If so, it depends on the where do you want to trace things. Right now, Laminar, unfortunately, doesn't trace voice SDKs like OpenAI's realtime, but we're very actively looking into it.

However, if somewhere in your pipeline you have transcripts of your voice agents, then it shouldn't be that different from tracing any other agent. In this case, Laminar and Signals work perfectly.

We also trained and benchmarked flow-1 on a lot of multi-turn voice agent like conversations.