RL Finetuning
Train models against compilers, validators, and business rules — not human preferences. Models that pass your tests, not just sound good.
Generic finetuning doesn't cut it anymore.
What you've tried
- Prompt engineering that breaks on edge cases
- SFT with curated datasets that took months to build
- RAG pipelines that still hallucinate on critical queries
- Vendor APIs that can't be customized for your domain
What the frontier labs actually do
- Train against verifiable outcomes, not human preferences
- Use reinforcement learning to embed deep behaviors
- Build simulators that grade model outputs programmatically
- Ship models that pass tests, not just sound plausible
The frontier labs use RL internally — now you can too.
Beyond Supervised Finetuning
RL finetuning doesn't just pattern-match — it teaches models to reason toward correct outcomes.
No Curated Data Required
Unlike SFT, you don't need thousands of perfect prompt-response pairs. Define a reward function and let the model learn from trial and error.
Verifiable Reward Functions
Train against compilers, validators, unit tests, or business rules. If your correctness criteria can be coded, it can be a reward signal.
Deeper Behavior Embedding
RL embeds behaviors into model weights at a fundamental level — not just surface patterns that break under distribution shift.
Custom Tool Specialization
Fine-tune agents to use your internal tools, APIs, and workflows. Models learn the right tool, right parameters, right sequence.
Production-Ready Models
Open-weight models you own and deploy anywhere — your cloud, your infrastructure, your IP. No vendor lock-in.
Continuous Improvement Loop
Models improve over time with drift detection and automatic retraining triggers. Your model gets better as your data evolves.
From Task Definition to Deployed Model
Forward Deployed Playbook
We embed with your team to understand the task, define success criteria, and map out testable conditions.
Simulator & Reward Design
Define the scoring environment — compilers, validators, business logic — that grades your model's outputs.
GRPO Training Loop
Reinforcement finetuning with verifiable rewards. No reward model needed — just your ground truth.
Serve + Monitor
Production inference with continuous feedback, drift detection, and automatic retraining triggers.
End-to-End RL Services
Simulator-Verified Training
Models learn from test passes, not noisy labels. We build the simulator that grades your model — unit tests, compilation checks, business rule validators — and use it as the reward signal for RL training.
- Automated test-based scoring
- Domain-specific simulators
- Verifiable correctness guarantees
Context Graph Training
RL for memory-aware agents that maintain context across long interactions. We train models with knowledge graphs so they learn when to retrieve, when to reason, and when to act.
- Knowledge graph integration
- Long-context reasoning
- Memory-aware agent behavior
Production Inference
Optimized serving with low latency using vLLM, TensorRT-LLM, and custom quantization. Your finetuned model deployed on your infrastructure with the performance characteristics you need.
- vLLM & TensorRT optimization
- Custom quantization (GPTQ, AWQ)
- Sub-100ms latency targets
Continuous Monitoring
Drift tracking and automatic retraining triggers. We monitor your model in production, detect when performance degrades, and kick off retraining with updated reward functions.
- Real-time drift detection
- Automatic retraining pipelines
- Performance regression alerts
Why Teams Choose RL Over SFT
Smaller Models, Better Results
An RL-tuned 7B model can outperform a generic 70B on your specific task. Smaller, faster, cheaper to serve.
No Reward Model Needed
GRPO eliminates the need for a separate reward model. Your verifiable tests are the reward signal.
You Own the Weights
Full IP ownership of finetuned weights. Deploy anywhere — your cloud, on-prem, edge. No vendor lock-in.
Forward Deployed Engineer
A dedicated Infornce engineer embedded in your team. In your Slack, on your standups, shipping with you.
Start Your RL Pilot
Tell us about your task. We'll scope a pilot, define the reward function, and show you what RL finetuning can do for your team.