Agentic RL environments
How to design executable training environments whose task distributions, tool interfaces, and episode structure produce behavior that transfers to deployment instead of overfitting to the simulator.
Research
Research at LLMix
The engineering questions LLMix works through in client programs are also open research questions. These are the directions the company is actively investigating.
Active directions
Rewards and verifiers
How to construct learning signal that is informative and resistant to exploitation: programmatic verifiers, outcome and process rewards, judge calibration, and reward-hacking analysis.
Post-training method selection
When supervised adaptation, preference optimization, and online reinforcement learning are each justified, and how staged programs behave compared with single-method training.
Long-horizon agent reliability
How agent behavior degrades over long interactions, how to measure time-to-failure rather than terminal success alone, and how training changes those failure profiles.
Publications in preparation
LLMix is working on papers in these areas. Publications will be listed on this page when they are accepted and public, with links to the papers and their artifacts. Nothing is published under the company yet, and no results are claimed before they can be verified against a public text.