Technical landscape
A comparison of relevant models, papers, open-source projects, and competitors, with links to the underlying evidence.
Frontier AI Research Sprint / 1–3 weeks
AI Research Consulting
Make the next AI investment with evidence.
Resolve a specific model, architecture, or post-training decision with a source-linked technical landscape and a concrete next experiment.
Discuss this sprint ↗Scope, fee, and acceptance criteria agreed before work begins.
The decision
When this sprint is useful
- Which model or architecture fits our workload and constraints?
- Does the evidence support investing in RL or another post-training method?
- What is differentiated, and what can we build with existing open-source tools?
The handover
What you receive
Decision memo
A recommendation tied to your constraints, alternatives considered, and the assumptions that could change the decision.
Risk register
Known limitations, missing evidence, operational dependencies, and questions requiring a hands-on test.
Experiment plan
Prioritized experiments with success criteria, required inputs, and a practical sequence for your team.
Before we start
A clear scope on both sides
Useful inputs for scoping
- The decision and deadline
- Current architecture or product brief
- Budget, deployment, and data constraints
Scope boundaries
This sprint produces research and a recommendation. Model training, legal diligence, and production implementation need a separate scope.
Fees depend on the decision, system complexity, and access requirements. Share your budget range and timeline to help establish fit.
Our research
Explore the thinking behind the work
RL Scaling →
RL Scaling studies what happens when reinforcement learning systems grow across compute, data, model capacity, rollout volume, task diversity, and feedback quality. The central question is not simply whether more resources improve a policy, but which part of the learning loop becomes the next constraint. This topic tracks empirical scaling behavior, large training systems, and the operational lessons behind durable gains.
Post-training →Post-training turns a pretrained model into a useful system through supervised fine-tuning, preference optimization, reinforcement learning, distillation, and targeted data generation. The choice of method should follow the desired behavior, available feedback, and evaluation setup rather than a preference for one algorithm.
Scaling Laws →Scaling laws describe empirical relationships between resources and performance. In reinforcement learning, useful laws must account for interaction data, reward quality, environment diversity, optimization pressure, and failure under proxy objectives—not only parameter count and training compute.
Read our RL Scaling Bottleneck Map →Common questions
Before an engagement
Can this help us decide whether to use reinforcement learning?
Yes. The starting point is the task, available feedback, evaluation quality, and deployment constraints. The recommendation may be RL, a simpler adaptation method, or a baseline experiment to resolve uncertainty.
How is this different from a literature review?
The scope begins with a decision and ends with a recommendation and experiment plan. Papers and model reports are evidence for that decision, rather than an exhaustive reading list.
Do we need to know which model to use already?
No. A product brief, representative tasks, and constraints are enough to discuss scope. Any missing evidence is identified before the work is agreed.
Next step
Bring a real problem.
Start with the scope.
Describe the goal, constraints, and timeline. We’ll review the fit and discuss a useful next step.