Domain reasoning and expert judgment
Models that must internalize domain distinctions, apply expert criteria, or produce decisions that generic instruction following cannot sustain.
The service
Bespoke post-training programs for specialized models and agents.
LLMix combines supervision, preference learning, online reinforcement learning, environment engineering, and release evaluation into one reproducible model-development program.
Program inputs and outputs
You bring
- A base model, checkpoint, or tunable hosted model
- A target capability with a definable notion of success
- Domain signal: demonstrations, preferences, traces, rubrics, verifiable tasks, or a simulator
- Deployment and data constraints
The program returns
- A trained checkpoint, adapter, or tuned endpoint
- The environment, reward, and data systems that produced it
- Learning curves, ablations, and held-out evaluation
- A regression suite, model card, and reproducible handoff
From base model to released capability
Every program walks the same pipeline, entering and exiting at the stages the capability justifies. Each stage gates the next on held-out evidence.
Target behavior
The capability, its failure modes, and its success criteria
Data, environment, and signal
Mixtures, demonstrations, tasks, verifiers, and rewards
SFT warm start
Competence and format from demonstrations
Preference stage
Judgment sharpened on chosen-versus-rejected comparisons
Online RL where justified
GRPO or related methods inside the environment
Held-out capability and regression gates
Gains proven where the policy cannot have gamed them
Released model artifact
Checkpoint or adapter with reproducible training assets
Training stages in depth
Stage 1
Supervised adaptation
Requires demonstrations of the target behavior.
Supervised stages teach the model what good behavior looks like when it can be shown directly. They establish the warm start that later stages build on. The main risks are mixture imbalance, contamination between training and evaluation data, and demonstrations that do not match the deployment distribution.
- Continued and domain-adaptive pretraining
- Instruction-data design
- Supervised fine-tuning, full-parameter and LoRA / QLoRA
- Multi-task, mixture-weighted, and curriculum SFT
- Synthetic-data generation and filtering
- Distillation and self-distillation
Stage 2
Preference optimization
Requires reliable judgments of better versus worse behavior.
Preference stages teach relative quality when no single ideal answer exists. They optimize chosen-versus-rejected data directly or through a trained reward model. The main risks are noisy or position-biased labels, weak judges, style collapse, and overfitting to the preference distribution.
- Human and AI preference collection
- Pair construction and hard-negative selection
- DPO and Online DPO
- KTO, ORPO, IPO, CPO, and related objectives
- Reward-model training
- Judge calibration and disagreement analysis
Stage 3
Reinforcement learning from feedback
Requires a reward source: human, AI, or learned.
RLHF and RLAIF optimize the policy against a reward model built from human or AI feedback. They shape open-ended behavior that verifiers cannot capture. The main risks are reward hacking, KL drift away from the warm start, and reward models that stop matching the behavior they were trained to represent.
- RLHF and RLAIF
- Constitutional and principle-based feedback
- Outcome, process, and generative reward models
- PPO and RLOO
- Reward and KL tuning
- Multi-objective alignment and online data refresh
Stage 4
Verifiable and agentic RL
Requires an environment where behavior can be exercised and scored.
When success can be checked programmatically, the reward is objective and hard to game. GRPO scores groups of sampled completions, and RLVR describes the broader verifiable-reward setting. This is the core stage for tool use and long-horizon agents. The main risks are verifier loopholes, sparse signal, and environment behavior that does not transfer to deployment.
- GRPO, DAPO, and GSPO
- RLVR with programmatic verifiers
- Multi-environment and tool-use RL
- Long-horizon agent training
- Curriculum and adaptive sampling
- Asynchronous and composite reward functions
Rollout and distributed training infrastructure
Online RL couples rollout generation, inference serving, reward computation, policy updates, checkpointing, and evaluation into one system. LLMix builds and operates that system with standard components, selected per program rather than all at once.
- PyTorch distributed training
- Hugging Face Transformers and TRL
- vLLM or SGLang-backed sampling
- Ray orchestration
- FSDP, DeepSpeed, or Megatron-style backends
- Policy and inference weight synchronization
- Checkpoint and optimizer recovery
- Experiment tracking and lineage
- Reproducible containers and configurations
- Serving conversion and quantization
Model release and regression gates
A checkpoint ships only when held-out capability evaluation shows the intended gain and the regression suite shows no unacceptable loss elsewhere. Release evidence includes learning curves, ablations, adversarial evaluation, cost accounting, a model card with known limitations, and rollback criteria.
Best-fit engagements
Post-training is most valuable when behavior can be exercised and judged. These are the situations where programs pay off.
Tool use and structured action
Models that must select tools, construct valid calls, use results, follow action schemas, and recover from tool or execution failures.
Long-horizon planning and recovery
Agents that must maintain goals and state across many turns, manage delayed consequences, revise plans, and avoid compounding mistakes.
Policy, safety, and operational constraints
Models that must follow explicit behavioral rules, abstain or escalate appropriately, satisfy verifiable constraints, or balance multiple objectives.
Teams with unsuccessful prior tuning
Programs that trained but did not deliver: LLMix audits data, rewards, environments, rollouts, and evaluation to find where the signal broke.
Delivered artifacts
A trained model is only useful when the result is reproducible.
Training data
- Versioned train, validation, and test sets
- Mixture and provenance manifests
- Preference or supervision pipelines
- Filtering and adjudication rules
Environment and signal
- Containerized task environment
- Tool and resource interfaces
- Task generators and reset logic
- Verifiers, rewards, or trained reward models
Training system
- Reproducible configurations
- Rollout and optimization infrastructure
- Checkpointing and recovery setup
- Experiment logs and lineage
Model artifact
- Full checkpoint, adapter, or tuned endpoint
- Tokenizer and template changes where applicable
- Inference conversion or quantization where scoped
Evidence and handoff
- Learning curves and ablations
- Held-out, adversarial, and regression evaluation
- Model card and known limitations
- Code, containers, documentation, and handoff
Technical questions, answered directly
Yes. For agentic post-training, LLMix can build the task environment, tool interfaces, state transitions, task generators, reset behavior, verifier, reward functions, rollout capture, and integration with the training loop.
Engagements may use open-weight models or hosted models that expose suitable supervised or reinforcement-tuning interfaces. Selection depends on model license, architecture, context length, tool requirements, deployment constraints, and available compute.
SFT is appropriate when high-quality target demonstrations exist. Preference optimization is useful when relative quality is easier to specify than a single ideal answer. Online RL is most valuable when behavior can be generated, exercised, and scored inside an environment. Many programs use these stages sequentially.
Yes, provided the task can be represented through controlled state, tools, episode boundaries, and a meaningful reward or verifier. The main engineering work is usually environment fidelity, rollout throughput, and credit assignment.
Yes. Depending on the task, supervision can come from deterministic checks, unit tests, structured state comparison, expert rubrics, human preferences, AI feedback, learned reward models, or combinations of these.
Yes. LLMix can audit data mixtures, labels, reward distributions, environment behavior, rollout traces, KL dynamics, sampling, checkpoints, evaluation leakage, and training-inference mismatch.
Related pages
Custom LLM fine-tuning
The supervised side in depth: SFT, LoRA and QLoRA, distillation, and data-mixture design.
RL environments for LLM agents
The executable worlds behind the online stages: tasks, tools, verifiers, rollouts.
SFT vs DPO vs GRPO
The method-selection guide behind the staging decisions on this page.