From Jim Fan · NVIDIA GEAR
Robotics is
pre-Chinchilla
Language models come with a recipe: how much data to pair with how many parameters. Embodied AI has no such curve yet, and finding it is itself a research frontier.
The thesis
The scaling laws for embodied AI are yet to be studied. Mapping them is the work.
In language modeling, scaling laws made training predictable: loss falls smoothly with parameters, data, and compute. The Chinchilla result reset the field: for a fixed compute budget, scale data and parameters together, roughly twenty tokens per parameter. Robotics has no equivalent map. Jim Fan is explicit that the embodied scaling law is an open research goal, not a known input to planning.
The LLM recipe
Compute in, capability out, predictably.
Kaplan (2020) and Chinchilla (Hoffmann et al., 2022) showed that language-model loss follows smooth power laws. Chinchilla's headline: most models were undertrained. Pair every parameter with roughly 20 tokens. Capability planning became engineering rather than gambling.
- Superpower
- Predictable returns
- Blind spot
- Proven for text only
Every frontier lab sizes its training runs against these curves before committing compute.
The robotics tangle
Too many axes to scale at once.
An embodied scaling law must span model size, simulation hours, real-fleet hours, embodiments, and the mixture across all three data fuels. There is no canonical "token of action," and the metric that matters is task success rate, which is expensive to evaluate and not guaranteed to track loss.
- Hard part
- Heterogeneous data
- Harder part
- Success rate ≠ loss
Real-robot data is bound to wall-clock time: a robot can collect at most 24 hours per day, and usually far less.
The wager
Emergence should arrive here too.
Fan's bet is that the LLM pattern repeats: tokenize actions well, compress them with a transformer like any other modality, and emergent properties appear as data and model size scale. The embodied Chinchilla is a result waiting to be measured.
- Bet
- Actions scale like text
- Status
- Active research
Until the curve exists, every data budget in robotics is a hypothesis.
The trade space
One field has a map. The other has a frontier.
| Dimension | Language models | Embodied AI |
|---|---|---|
| Scaling recipe | ||
| Unit of data | Tokens | Trajectories & action tokens |
| Capability proxy | Validation loss | Task success rate |
| Data supply |
My synthesis
Whoever maps the curve, budgets the future.
Until an embodied scaling law exists, robotics data strategy is a portfolio of bets. The rational response is to run the experiment deliberately: small pilots across a grid of model sizes and data volumes, fitted curves, and only then large spend on what the curve rewards.
-
01
TokenizeMake actions a first-class modality
-
02
PilotSmall runs across model × data grids
-
03
FitMeasure capability vs data and parameters
-
04
SpendScale only what the curve rewards
Strategic implication
A data engine should discover its own scaling law.
For a company whose advantage includes demonstration data, "how much data is enough?" has direct capital consequences. Instrumenting collection, including marginal task success per additional demonstration-hour for each task family and embodiment, turns operations into the experiment that reveals the curve.
A controlled robot-training program is the place where those curves can be measured systematically: controlled tasks, repeatable evaluation, and a steady stream of demonstrations to plot against capability.
LLMs have a recipe. Robotics still has a hypothesis.