We’re building Leibler, a harness that turns production agent traces into training data and environments, then post-trains smaller models for your actual workload.
Production traces → curated data & environments → evals → post-training → cheaper specialized model -> deployment
Our hypothesis: a post-trained Qwen3.8-27B can handle ~90% of production tasks at frontier-level performance, while cutting inference costs by up to 10×.
We have proved this with one of our pilots where we decreased their costs from $50k/month to $4.6k/month, we believe this is the closest we have gotten to self evolving agents.
We’re looking for teams running agents in production to test this with us.
Fine tuning is a part of it, but environment generation and evaluation so that the training data matches the production is the hardest part. After the data comes in we can try OPD, RL, SFT to understand what method is the best one. Is this something you would like to try on your production setting ?
We’re building Leibler, a harness that turns production agent traces into training data and environments, then post-trains smaller models for your actual workload.
Production traces → curated data & environments → evals → post-training → cheaper specialized model -> deployment
Our hypothesis: a post-trained Qwen3.8-27B can handle ~90% of production tasks at frontier-level performance, while cutting inference costs by up to 10×.
We have proved this with one of our pilots where we decreased their costs from $50k/month to $4.6k/month, we believe this is the closest we have gotten to self evolving agents.
We’re looking for teams running agents in production to test this with us.
https://leibler.dev
So fine-tuning as a service (TaaS)?
Fine tuning is a part of it, but environment generation and evaluation so that the training data matches the production is the hardest part. After the data comes in we can try OPD, RL, SFT to understand what method is the best one. Is this something you would like to try on your production setting ?