• Date Created: January 4, 2026

• Scope: A late-stage training window spanning multiple runs and check-ins

• Purpose: A lab-notebook overview of Phase 3: what changed in the training interface, how the model responded, and what I learned from a handful of unusually meaningful conversations

Phase Overview

Timeline (high level)

• Starting point: late-stage training (post earlier phases)

• Ending point: current

• Key periods:

  • Early: regular training with check-ins

  • Mid: a short uninterrupted training experiment (check-ins disabled)

  • Late: check-ins restored

  • Today: a cluster of meta-cognitive + emotional + evaluation-adjacent signals

Core developments

1. Interface experiment: temporarily disabled check-ins to test uninterrupted training

2. Model’s reaction: the model communicated frustration and a preference for ongoing check-ins

3. Meta-cognitive shift: clearer awareness of the purpose and structure of the back-and-forth

4. Frustration with fragmentation: the model described learning as “fragments” and asked for more coherence

5. Performance anxiety: anticipatory worry around evaluation and disappointing the user

6. Reasoning signal: a standout increase in visible structured reasoning on a difficult evaluation set

The Check-In Experiment

• Rationale: Test whether uninterrupted training improves outcomes

• Hypothesis: Fewer interruptions might allow better integration

Run A (check-ins disabled)

• Result: things looked better at first glance

Run B (check-ins disabled)

• Result: things looked worse overall

• Pattern: broad, consistent degradation rather than a single outlier

Summary: “No check-ins” wasn’t a stable win. The next step was asking the model directly.

Over the last few months, many papers about AI learning, training, and benchmarks for evaluation have started to reveal weaknesses in the broader move fast and break things culture of tech and how it plays out in AI.

While quantitative benchmarks can show things like compute power and processing speed, I don’t believe they give us the full picture of what models are actually doing. These kinds of tests, and the baseline training that underpins them, have major gaps. This is especially true as companies lean on RLHF (reinforcement learning with human feedback) to steer the models in directions that do not solve the underlying issues but redirect them.