Surface forfeit-rate log-linear fit in lab_status for pretraining runs

[thomas-schweich]

Summary

The log-linear fit on forfeit rate has become the most informative late-stage convergence signal for the 512-ctx pretraining runs — it keeps moving after val_loss, accuracy, and legal/late_legal rates have all plateaued. The dashboard chart surfaces it, but the `lab_status` MCP tool didn't, so a monitoring agent couldn't see the trend without manually parsing val log lines.

This PR adds a `pretrain` block to each running `run_type=pretrain` row in `lab_status`, carrying:

• `latest`: game_completion_rate, avg_plies_completed, forfeit_ply min/max/median, legal/late_legal, val_loss from the most recent val record
• `forfeit_fit`: log-linear regression over the most recent half of the (step, forfeit_rate) series — reports slope_per_step, half_life_steps, current_forfeit, and n_points. The fit window matches the dashboard's second-half-of-history heuristic.

Example output on the live 512-ctx run at step 272,000:

```json
{
"latest": {
"step": 272000,
"val_loss": 2.887,
"game_completion_rate": 0.961,
"avg_plies_completed": 335.3,
"forfeit_ply_min": 28,
"forfeit_ply_max": 438,
"forfeit_ply_median": 120
},
"forfeit_fit": {
"slope_per_step": -1.11e-05,
"half_life_steps": 62368,
"n_points": 66,
"current_forfeit": 0.0391
}
}
```

Implementation

• New `read_pretrain_val_summary(trial)` helper in `pawn/lab/monitor.py` scans `metrics.jsonl` directly — metrics.jsonl is the source of truth, so there's no new `Trial` state to track and no drift across resume/recovery.
• `runner.status()` attaches the summary under `pretrain` only when `run_type == "pretrain"` and when val records with `game_completion_rate` exist (adapter runs and pre-first-eval pretraining trials skip it silently).
• `lab_status` tool docstring updated so the agent knows the new field exists.

Test plan

• `pyright pawn/lab/{monitor,runner,server}.py` clean
• `pytest tests/lab` — 250 passed
• Verified on the live trial 23 metrics file: helper returns the expected shape and the fit matches the dashboard (hl ≈ 62K at step 272K)
• CI passes

[thomas-schweich]

@claude

[claude]

Claude finished @thomas-schweich's task in 1m 48s —— View job

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Code Review

• Read changed files
• Review implementation
• Post findings

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Overall this is clean and well-scoped. The implementation is straightforward, the docstrings are good, and the integration into status() is minimal. A few things worth flagging:

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1. Redundant local import math as _math (monitor.py:192)

math is already imported at the top of the file (line 8). The local re-import inside the function body is unnecessary.

# Line 8 — already there
import math

# Line 192 — redundant
import math as _math

Replace all _math.log / _math.log(2) calls with math.log / math.log(2). Fix this →

---

2. No test coverage for read_pretrain_val_summary

tests/lab/test_monitor.py doesn't import or test the new function. TestStatus in test_runner.py also has no test for the pretrain block being attached to pretraining rows. For a function doing manual OLS regression over file data, at minimum you'd want:

• returns None with no run_dir
• returns None with no metrics.jsonl
• returns only latest when n < 4
• forfeit_fit fields are correct for a known (step, gc) series
• all-zero forfeit rates (100% completion) → no forfeit_fit
• runner.status() includes pretrain key when run_type == "pretrain" and omits it for adapter runs

The PR description mentions "Verified on live trial 23" which is good, but that's not a regression guard.

---

3. current_forfeit comes from forfeit_rates[-1] (full series), not the fit window

summary["forfeit_fit"] = {
    ...
    "current_forfeit": forfeit_rates[-1],   # full-series last, not half-window last
}

This is consistent and probably what you want (most recent observed value regardless of window). However, as the model approaches 100% completion — when the last overall record has forfeit_rate == 0.0 but the second-half window has positive values — the pos filter will exclude that zero and the fit would still be computed, but current_forfeit reports 0. That's actually fine and informative. Just worth being explicit about in the docstring.

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4. Docstring lists forfeit_fit fields as {slope, half_life_steps, n_points} but the actual dict also has current_forfeit

Minor, but the docstring in read_pretrain_val_summary omits current_forfeit from the listed fields. The lab_status tool docstring in server.py does list it correctly. Fix this →

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5. Performance: full file scan on every status() call

read_pretrain_val_summary opens and reads metrics.jsonl from byte 0 every time lab_status is called. read_metrics avoids this via offset tracking. At 272K steps with eval every ~2K steps, that's ~130+ lines to scan each time. For a monitoring tool called by an agent every few seconds this is probably fine, but it's worth noting the asymmetry. If it becomes a problem, the summary could be cached on the Trial object and invalidated by the existing offset mechanism.

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Summary: Items 1 and 4 are small and easy to fix; item 2 (missing tests) is the only thing I'd consider blocking given that the regression logic is non-trivial. Items 3 and 5 are informational. The overall design — reading from metrics.jsonl directly as the source of truth, attaching the block only for pretraining runs with sufficient val history — is correct.

[thomas-schweich]

@claude please re-review

[claude]

Claude finished @thomas-schweich's task in 1m 40s —— View job

---

Code Review

• Read changed files
• Compare current state against previous review findings
• Post updated review

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All four actionable items from the first review are resolved. Here's the full accounting:

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1. Redundant import math as _math — FIXED

The local re-import is gone. monitor.py uses the module-level import math throughout read_pretrain_val_summary (lines 199, 207).

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2. Missing tests — FIXED

TestReadPretrainValSummary in test_monitor.py now covers every case I flagged:

• test_no_run_dir_returns_none / test_missing_metrics_file_returns_none
• test_no_val_records_returns_none / test_val_records_without_game_completion_return_none (adapter silencing)
• test_returns_latest_without_fit_when_too_few_records (n < 4)
• test_returns_fit_on_known_series — exact exponential decay, slope verified to rel=1e-6
• test_all_zero_forfeit_omits_fit (100% completion → no forfeit_fit)
• test_latest_records_carries_all_fields
• test_current_forfeit_is_last_of_full_series

test_runner.py adds test_status_attaches_pretrain_block_for_pretrain_runs and test_status_omits_pretrain_block_for_adapter_runs. Both are correctly wired.

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3. current_forfeit from full series vs. fit window — Documented + tested

The docstring (monitor.py:131–135) now explicitly calls this out. The new test_current_forfeit_is_last_of_full_series test constructs a series ending in forfeit=0.0 and asserts that current_forfeit reports 0.0 even when the OLS window excluded that point. Behavior is intentional and verified.

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4. Docstring missing current_forfeit — FIXED

monitor.py:125–128 now lists {slope_per_step, half_life_steps, n_points, current_forfeit} correctly.

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5. Performance: full file scan — No change (informational, acceptable)

Still a full scan from byte 0 on every status() call, as before. Fine at current scale; caching on Trial remains a valid future optimization if it becomes a bottleneck.

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One minor nit not worth blocking on: test_current_forfeit_is_last_of_full_series wraps the key assertion in if "forfeit_fit" in summary:, which means the test passes vacuously if the fit is skipped. Given the input is constructed so that the fit will be computed (11 records, 5 positive-forfeit points in the half-window), this is fine in practice — but an unconditional assert "forfeit_fit" in summary before the field check would make the test intention explicit. Not blocking.

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LGTM. All blocking issues resolved, test coverage is solid, implementation is correct.