Pilot-0.3 — continued training
This is not a third independent model birth. It is continued training of Pilot-0.2 / Local-70M-0 — the same 69,970,848-parameter model, resumed from that model’s final checkpoint and trained to 1,000,013,824 cumulative tokens.
What this run is
This is not a third independent model birth
It is continued training of Pilot-0.2 / Local-70M-0 — the same 69,970,848-parameter model, started from that model’s final checkpoint.
There is no new initialization and no new seed. Counting this as a third birth would turn two independent observations into three.
- Pilot-0.3 is not a new initialization.
- Pilot-0.3 is not a new seed model.
- Pilot-0.3 started from the Pilot-0.2 final checkpoint.
- Pilot-0.3 includes a second exposure to the same corpus.
- Pilot-0.3 had its learning-rate schedule restarted.
- Parent model
- Pilot-0.2 / Local-70M-0
- Parameters
- 69,970,848 identical to the parent — same model
- Cumulative tokens
- 1,000,013,824
- Cumulative steps
- 61,036
- Final validation CE
- 2.557595558464527
Explicit confounds
This is not a controlled token-only intervention. The schedule restart is one of the changed conditions, so Pilot-0.2 and Pilot-0.3 cannot be compared as two different births.
The run includes a second exposure to the same corpus and a restarted learning-rate schedule. Any observed change therefore has at least two candidate causes, and this experiment cannot separate them.
Validation curve
| Point | Cumulative step | Validation CE |
|---|---|---|
| Pilot-0.2 source final | 32,470 | 2.6221997812 |
| 1% | 32,756 | 2.6835686937 |
| 5% | 33,899 | 2.7087057121 |
| 10% | 35,327 | 2.7068712562 |
| 25% | 39,612 | 2.6759214178 |
| 50% | 46,754 | 2.6224451177 |
| 75% | 53,895 | 2.5734781809 |
| 100% | 61,036 | 2.5575955585 |
The validation regression in the first section happened at the same time as the schedule restart. CE later fell past the source level to 2.55760. Because there is no control arm, the cause cannot be attributed to the schedule alone or to the second corpus pass alone.
Measured profile shift
In plain language
Training the same model for longer made its general language score better — and made two of the measured signals worse.
The two measures that had grown most during the original training (I and A) both declined across the continuation, while an easy measure (R) rose. So “train longer, get better at everything” did not hold here.
This is one continuation of one model, measured on the same questions. It is an example, not a rule about training in general.
Technical evidence
Matched items, n=128 per cell, at the 25%, 50% and 75% checkpoints of the continuation.
- I@8: +0.452 → +0.371 → +0.315
- A@0: +0.325 → +0.283 → +0.267
- R@1: +0.141 → +0.109 → +0.266
- Validation CE: 2.6759 → 2.6224 → 2.5735
The general language-model validation metric improved while the measured I and A signals declined and easy R rose. This profile shift rests on a single continuation lineage, the same items, and n=128 diagnostic sampling.
Correct reading: this happened in this continuation lineage. Incorrect reading: continued training harms reasoning.
| Checkpoint | M@64 | R@1 | I@8 | C@True | Q@1 | V@8 | A@0 | T@modulus |
|---|---|---|---|---|---|---|---|---|
| 25% | 41/128 +0.094 | 73/128 +0.141 | 60/128 +0.452 | 3/128 −0.008 | 5/128 −0.024 | 20/128 +0.036 | 47/128 +0.325 | 14/128 +0.095 |
| 50% | 41/128 +0.094 | 71/128 +0.109 | 50/128 +0.371 | 2/128 −0.016 | 4/128 −0.032 | 20/128 +0.036 | 42/128 +0.283 | 14/128 +0.095 |
| 75% | 43/128 +0.115 | 81/128 +0.266 | 43/128 +0.315 | 5/128 +0.008 | 6/128 −0.016 | 20/128 +0.036 | 40/128 +0.267 | 15/128 +0.103 |
| 100% | UNMEASURED — the final checkpoint and receipt exist, but no capability artifact was produced (source §8.5). Not zero, not a failure. | |||||||
Final capability state
The 100% final checkpoint is UNMEASURED
The durable diagnostic job requested and completed only the 25%, 50% and 75% checkpoints. The 1%, 5% and 10% checkpoint files exist as training records but no capability battery was run on them. The 100% final checkpoint and its receipt exist, but there is no n=128 or n=800 capability artifact for it, and the durable records do not contain a sufficient reason for why the final scan was not run.
The 100% checkpoint therefore cannot be called a failure. The correct label is unmeasured. Filling the missing cell with a zero score, or presenting 75% as if it were the final state, is not permitted.
Unmeasured is not zero, not chance, not a failure, and not an interpolated value. The 75% checkpoint is not presented as the final state of this model.
- Final checkpoint
- Exists; receipt matches
- Capability artifact
- Does not exist
- Correct label
- UNMEASURED
- What would close this gap
- Running the same frozen n=128 diagnostic scan on the 100% checkpoint — see Limits.
Training reliability
- Run status
- COMPLETED
- Final checkpoint size
- 915,599,240 bytes
- Hash receipt
- Final checkpoint hash matched the receipt.
- Non-finite gradient events
- 13
- Handling
- The 13 non-finite gradient events were handled by same-batch GradScaler backoff and the run completed.
These events are visible in the fail-closed training records and were not hidden from the results.
These events are reported because they happened, and they are not inflated into a claim that the run was unstable. The run completed, and the final hash matched its receipt.
Source: Scientific Evidence Inventory v1.0 (evidence cutoff 30 August 2026), §7.1, §7.2, §7.3, §7.4, §8.5.