Bespoke post-training for models and agents
LLMix designs and runs custom post-training programs: from data mixtures and supervised warm starts to preference optimization, reward systems, RL environments, online reinforcement learning, and validated model release.
From base model and target behavior to trained model artifact and reproducible training system.
Open-weight and supported hosted models · Single-turn, multi-turn, and tool-using agents
You bring
Bespoke post-training system
data → SFT → preferences → RL
environment ↔ rollout ↔ reward
The program delivers
One complete program
LLMix does not sell an isolated fine-tuning run. A post-training program combines the supervision, environment, reward, optimization, and release evidence required to make a capability reproducible.
You define
The behavior the model must learn, the failure it must stop producing, the constraints it must obey, and the conditions under which it must work.
LLMix builds
Data mixtures, demonstrations, preferences, environments, verifiers, rewards, rollout workers, optimization stages, and evaluation gates.
The program delivers
A trained checkpoint, adapter, or tuned endpoint together with reproducible configurations, training assets, regression tests, and release documentation.
Post-training engineering
Build the examples and comparisons that define the target behavior.
Create executable tasks in which an agent can act, use tools, observe consequences, fail, recover, and generate trajectories.
Translate success into a learning signal that is informative, calibrated, and resistant to exploitation.
Run the coupled inference-and-training system reliably enough to support controlled learning.
Agentic RL environments
For tool-use and long-horizon capabilities, LLMix builds the environment in which the policy is trained: task state, observations, actions, tools, transition logic, episode boundaries, task generators, verifiers, rewards, and trajectory records.
An RL environment is not a workflow diagram. It is executable training infrastructure.
Task generator
produces the episode
Observation → Policy → Action / tool call
the model acts
world state · transition · tools · reset
Verifier + reward
scores the behavior
Trajectory store
captured for training
Method selection
Post-training is usually staged. The program begins with the strongest justified warm start, then adds preference or online reinforcement learning only where the task and supervision support it.
01 · Demonstrate the target
Use continued or domain-adaptive pretraining, SFT, parameter-efficient tuning, curriculum data, and distillation when high-quality target behavior can be demonstrated directly.
02 · Teach relative quality
Use preference pairs and direct preference objectives when experts, users, or AI judges can reliably distinguish better behavior from worse behavior even when no single ideal answer exists.
03 · Learn through action and feedback
Use online rollouts when the model can act inside an environment and receive outcome, process, or verifier-based rewards. This is the core setting for tool use, long-horizon behavior, and RL from verifiable rewards.
Selected vertical applications
LLMix applies the same environment, signal, and optimization machinery to domain-specific capabilities. Initial engineering blueprints focus on cybersecurity, insurance, and legal systems.
Defensive monitoring, investigation, and containment trained inside simulated infrastructure.
Explore the blueprintCase reasoning, evidence gathering, and structured recommendations trained from expert decisions.
Explore the blueprintCitation-grounded analysis, issue coverage, and abstention trained from expert supervision.
Explore the blueprintLLMix is a founder-led engineering practice built on more than 15 years of production software and systems experience. It combines hands-on model experimentation, agent and environment engineering, distributed systems work, and rigorous evaluation in one post-training program.
Research on post-training methods and agentic environments is in preparation and will be published on the research page.