> Agent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles
my brain shut down and bounced minus the comment because it's a launch and maybe you'd want feedback.
I'd be using this for post-training in a custom harness: so what you focused on during post-training and what it excels at are the most interesting things
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.