PretrainingPretraining

Pretraining produces the base model that downstream stages specialize. Its purpose is to absorb broad statistical regularity from large, mixed-modality corpora before any task-specific signal is applied.

Compute-optimal scaling

Resource allocation between model size and training tokens follows the compute-optimal regime. For compute budget $C$ in FLOPs, training loss $\mathcal{L}$ is bounded below by

where $N$ is parameter count, $D$ is training token count, and $(E,A,B,\alpha ,\beta )$ are fitted constants. The Chinchilla operating point, $\alpha \approx 0.34$, $\beta \approx 0.28$, implies the compute-optimal allocation

i.e. compute should be split approximately evenly between scaling parameters and scaling data. Earlier work recommended a parameter-heavy split that is empirically suboptimal at fixed compute.

The codex's base model targets the compute-optimal point for the available training budget per phase, with two deliberate adjustments:

1. Inference-cost weighting. Because the model is served continuously, a smaller-than-optimal model is preferable when inference cost dominates training cost over the deployment lifetime. The codex's models are typically 20–40% smaller than the strict compute-optimal point.
2. Modality-aware token counting. Multi-modal tokens are not interchangeable. Pretraining accounts for token "exchange rates" measured empirically: an image patch token contributes approximately 0.3–0.6× as much downstream signal as a language token, depending on task.

Corpus

The pretraining corpus is a mixture of public and validator-contributed sources:

The exact mixture shifts per pretraining run and is published with each checkpoint as a manifest hash. License compliance is enforced at the manifest level: every source carries a license tag and incompatible sources are excluded by the build pipeline.

The embodied demonstration share is the codex's unique contribution. No centralized lab has access to a comparable distributed source of embodied data, and the share grows monotonically as the validator network expands.

Objectives

Pretraining minimizes a weighted sum of objectives:

with:

• ${\mathcal{L}}_{\mathrm{LM}}$: next-token cross-entropy on text + code.
• ${\mathcal{L}}_{\mathrm{vision}}$: masked-patch reconstruction + contrastive.
• ${\mathcal{L}}_{\mathrm{align}}$: cross-modal contrastive between vision and language latents.
• ${\mathcal{L}}_{\mathrm{world}}$: next-chunk latent prediction conditioned on a learned null action.
• ${\mathcal{L}}_{\mathrm{embod}}$: flow-matching on embodied demonstration trajectories.

The loss weights ${\lambda }_i$ are tuned per phase to balance gradient norms across objectives; a Lagrangian-style auto-tuner adjusts them on the fly to keep no single objective from dominating.

Optimization

Pretraining uses AdamW with the standard cosine schedule:

after a linear warmup over the first 2% of steps. Gradient clipping at $\Vert \nabla {\Vert }_2=1.0$. Batch size scales with compute, capped at the critical batch size for the data distribution.

Distributed training

Pretraining at scale is sharded with 3D parallelism: data parallel across replicas, tensor parallel within nodes, pipeline parallel across stages. The codex uses a custom topology adapter that auto-discovers the validator cluster's interconnect and chooses sharding to maximize HBM bandwidth utilization.

This is the most network-intensive computation in the system and is reserved for validators in the highest hardware tier; see Validators.

Checkpoints and lineage

Every pretraining run produces a sequence of checkpoints, each carrying:

• A content hash of the weights.
• A manifest hash of the training data.
• A hash chain back to the starting checkpoint (the run's lineage).
• An eval-suite score vector against the standard benchmarks.

Lineage is verifiable: any third party can re-derive the checkpoint hash from the lineage and a small set of intermediate proofs.