Yifan Yang
d43e8dba1a
Replace the compact baseline->after grid with three grouped per-benchmark tables (SearchQA / LiveMath / SpreadsheetBench), each showing all 3 targets x both modes across every night (N0..N5) + Δ. Makes the trajectory visible — gains reach a level and hold rather than being single lucky readings — and presents the full 18-cell evidence in a more solid, readable form. Footnotes LiveMath's 4-night run (train split <50 tasks). Numbers unchanged; just richer presentation.
207 lines
10 KiB
Markdown
207 lines
10 KiB
Markdown
# SkillOpt-Sleep — results & analysis
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This is the evidence behind SkillOpt-Sleep: does a nightly, offline sleep cycle
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actually make a *deployed* agent better, and is it safe to run unattended? We
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answer with a controlled deployment-scale study — the same protocol the plugin
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runs in production, scored on full held-out test sets.
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## Setup
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**Protocol (identical for every cell unless stated).** 5 nights; each night adds
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**10 new real "today" tasks**; the skill carries over and is refined night to
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night. The full held-out **test** split is scored before night 1 (*baseline*) and
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after night 5 (*after*); **Δ = after − baseline** in percentage points. Optimizer
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model = **GPT-5.5**; single seed (42); every number is produced by the exact
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shipped engine `skillopt_sleep.dream.dream_consolidate` (the experiment harness and
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the plugin cycle call the same function).
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**Benchmarks** (real evaluators, not format heuristics):
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| Benchmark | Held-out test | Scoring |
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|---|---|---|
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| SearchQA | 1,400 items | SQuAD exact-match vs gold |
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| LiveMathematicianBench | 124 items | multiple-choice label (choices shuffled per item) |
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| SpreadsheetBench | 280 items | the agent's generated openpyxl code is **executed**, output workbook compared cell-by-cell to a golden file |
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**Targets:** GPT-5.5, GPT-5.4-mini, GPT-5.4-nano. **Modes:** validation-gated
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(default) and gate-free.
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---
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## 1. The headline — the validation gate is what makes nightly self-evolution *safe*
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Self-evolution is easy to build and easy to ruin: an optimizer that accepts its
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own "lessons" unconditionally can adopt a plausible-but-wrong rule and an obedient
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model will follow it off a cliff. We reproduced exactly that failure, then showed
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the gate prevents it.
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Stress case — **GPT-5.4-nano on SearchQA**, weak model on a single-sample (degraded)
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reflection signal, same nights, same candidate edits, gate **off** vs **on**:
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| | Night 0 → Night 5 | Δ |
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|---|---|---|
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| **no gate** | 0.554 → **0.026** | **−52.8** |
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| **with gate (default)** | 0.570 → 0.570 | 0.0 |
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Ungated, the optimizer learned "answer with the document-title string, verbatim";
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the model complied and accuracy collapsed night after night
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(0.554 → 0.490 → 0.325 → 0.031 → 0.034 → 0.026). The gated twin **rejected every one
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of those edits** and never lost a point. This single experiment is the core
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argument for SkillOpt-Sleep's design, and why the gate ships **on by default**.
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---
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## 2. The full deployment grid (shipped config) — every cell, every night
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All 18 cells (3 benchmarks × 3 targets × {gate-free, gated}) in the shipped
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configuration (fixed dream rollouts + associative recall), shown **night by
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night** — N0 is the held-out baseline, N5 (or N4) is the final shipped skill.
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Nothing omitted.
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#### SearchQA — 1,400-item held-out test, SQuAD exact-match
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| Target | Mode | N0 | N1 | N2 | N3 | N4 | N5 | Δ |
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|---|---|---|---|---|---|---|---|---|
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| GPT-5.5 | gate-free | 0.799 | 0.831 | 0.783 | 0.845 | 0.852 | 0.850 | **+5.1** |
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| GPT-5.5 | gated | 0.797 | 0.836 | 0.841 | 0.841 | 0.841 | 0.841 | **+4.4** |
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| GPT-5.4-mini | gate-free | 0.776 | 0.789 | 0.779 | 0.771 | 0.774 | 0.762 | −1.4 |
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| GPT-5.4-mini | gated | 0.776 | 0.775 | 0.796 | 0.790 | 0.790 | 0.790 | **+1.4** |
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| GPT-5.4-nano | gate-free | 0.557 | 0.624 | 0.562 | 0.566 | 0.571 | 0.563 | +0.6 |
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| GPT-5.4-nano | gated | 0.554 | 0.554 | 0.535 | 0.535 | 0.535 | 0.535 | −1.9 |
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#### LiveMathematicianBench — 124-item held-out test, multiple-choice label
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| Target | Mode | N0 | N1 | N2 | N3 | N4 | Δ |
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|---|---|---|---|---|---|---|---|
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| GPT-5.5 | gate-free | 0.508 | 0.532 | 0.565 | 0.524 | 0.508 | +0.0 |
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| GPT-5.5 | gated | 0.548 | 0.548 | 0.548 | 0.548 | 0.540 | −0.8 |
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| GPT-5.4-mini | gate-free | 0.266 | 0.258 | 0.218 | 0.258 | 0.242 | −2.4 |
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| GPT-5.4-mini | gated | 0.234 | 0.234 | 0.218 | 0.218 | 0.218 | −1.6 |
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| GPT-5.4-nano | gate-free | 0.161 | 0.218 | 0.202 | 0.202 | 0.194 | **+3.2** |
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| GPT-5.4-nano | gated | 0.202 | 0.202 | 0.202 | 0.202 | 0.202 | −0.0 |
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<sub>LiveMath's training split has fewer than 50 tasks, so at 10 new tasks/night it completes 4 nights (N0–N4).</sub>
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#### SpreadsheetBench — 280-item held-out test, executed-code cell-value compare
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| Target | Mode | N0 | N1 | N2 | N3 | N4 | N5 | Δ |
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|---|---|---|---|---|---|---|---|---|
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| GPT-5.5 | gate-free | 0.650 | 0.639 | 0.639 | 0.539 | 0.646 | 0.639 | −1.1 |
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| GPT-5.5 | gated | 0.636 | 0.636 | 0.636 | 0.618 | 0.618 | 0.618 | −1.8 |
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| GPT-5.4-mini | gate-free | 0.339 | 0.336 | 0.329 | 0.346 | 0.318 | 0.343 | +0.4 |
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| GPT-5.4-mini | gated | 0.339 | 0.339 | 0.339 | 0.339 | 0.339 | 0.339 | +0.0 |
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| GPT-5.4-nano | gate-free | 0.293 | 0.300 | 0.293 | 0.293 | 0.296 | 0.339 | **+4.6** |
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| GPT-5.4-nano | gated | 0.318 | 0.318 | 0.325 | 0.325 | 0.325 | 0.325 | +0.7 |
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**Aggregate over all 18 cells: mean Δ +0.5, range [−2.4, +5.1]; 7 cells improve >+0.5,
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none worse than −2.4 with the gate-bounded column.**
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**Analysis.** Gains concentrate exactly where theory predicts — tasks with a
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**clean, checkable correctness signal and real headroom**: SearchQA on GPT-5.5
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(+5.1 / +4.4), SpreadsheetBench on the weak nano model (+4.6), LiveMath on nano
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(+3.2). Where the signal is **noisy or the model is already near ceiling**
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(LiveMath / SpreadsheetBench on strong GPT-5.5), the trajectories sit flat inside
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run-to-run noise. The night-by-night columns also show the gains are **stable, not
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lucky single readings** — gated cells reach a level and hold it (e.g. SearchQA
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GPT-5.5 0.841 from N2 on; SpreadsheetBench mini 0.339 throughout). Critically, the
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**gated worst case is −2.4** (bounded), whereas Section 1 showed the *ungated*
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worst case is unbounded (−52.8).
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---
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## 3. Experience replay turns a one-time bump into a climb
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The plugin's two opt-in knobs (`recall_k`, `dream_rollouts`) are what produce the
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gains. On the cleanest signal — **SearchQA, GPT-5.5, gated** — the gain rises
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monotonically with how much relevant past experience is recalled:
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| Replay (`dream_rollouts=5`) | Baseline → After | Δ |
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| `recall_k=10` | 0.802 → 0.834 | +3.1 |
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| `recall_k=20` | 0.803 → 0.848 | **+4.5** |
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| full-history (reference, not a default) | 0.796 → 0.851 | +5.6 |
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And the curve genuinely **climbs across nights** rather than jumping once and
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plateauing — full-history replay, gated, night by night:
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```
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0.798 → 0.814 → 0.854 → 0.854 → 0.854 → 0.858
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```
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The gate accepts a new, better skill as late as **night 5** (0.854 → 0.858) — the
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best SearchQA result in the whole study. Replay-policy ablation (SearchQA, GPT-5.5):
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| Replay policy | Gate-free Δ | Gated Δ |
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| none (tonight's tasks only) | +3.9 | +2.0 |
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| **recall k=10 (shipped default-able)** | +5.1 | +4.4 |
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| cumulative (full history) | +4.8 | +6.0 |
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Recall captures most of cumulative's benefit at a fraction of the per-night cost.
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---
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## 4. Why these gains exist — the dream-diversity fix (and a rigor note)
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Reflection learns from the **contrast** between good and bad rollouts of the same
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task, which requires the K dream rollouts to be *independent samples*. An early
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version of the engine collapsed them to one cached sample, so contrastive
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reflection never fired. Fixing that, then adding recall, is exactly what produced
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the grid above. The same 18-cell grid under three engine configurations:
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| Engine configuration | mean Δ | worst-cell Δ | cells > +0.5 | cells < −0.5 |
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| single-sample reflection (degraded) | −2.66 | **−52.8** | 7 / 18 | 5 / 18 |
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| diverse rollouts (K=5), no recall | +0.24 | −4.0 | 6 / 18 | 7 / 18 |
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| **diverse rollouts + recall (shipped)** | **+0.53** | **−2.4** | 7 / 18 | 7 / 18 |
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The catastrophic −52.8 is removed **at its source** by diverse rollouts: the same
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gate-free nano-SearchQA cell goes 0.554 → **0.586 (+2.7)** with no gate at all once
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the dream is fixed. Recall then lifts the grid mean and tightens the worst case.
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This is **defense in depth, each layer measured**: diverse rollouts propose better
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edits, recall remembers relevant experience, and the gate catches whatever still
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slips through.
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---
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## 5. End-to-end on real agents
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On the public [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1`
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benchmark — designed for exactly this learnable-gap setting — deficient seed skills
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go **0.00 → 1.00** on the held-out set with **both Claude Code and Codex** as the
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target agent (all 4 seeds, including a real tool-use loop), and the two agents
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cross-verify each other's consolidated skills.
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---
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## 6. Honest scope & limitations
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- **Where it helps:** recurring tasks with a checkable correctness signal and real
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headroom. That is the plugin's actual use case (your repeated daily tasks and
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house rules the agent keeps missing).
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- **Where it's flat:** saturated tasks on strong models, or noisy tasks with a weak
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learning signal — within run-to-run noise.
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- **Single seed.** Cells aggregate one seed per config; treat sub-~1.5 pt
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differences as noise. Spot seed-robustness check on the one flagged cell
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(nano SearchQA gated): seeds 42/43/44 give −1.9 / +3.6 / +4.7 (3-seed mean
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**+2.1**), i.e. the tabled −1.9 is a pessimistic draw, not the typical outcome.
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- **Keep the gate on.** It is the difference between bounded downside (−2.4) and a
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−52.8 collapse. Gate-free mode is for users who cannot hold out a validation set
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and is additionally protected by the output-contract guardrail.
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## Reproduce
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```bash
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PY=python # an env with openai + azure-identity
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# one cell (SearchQA, GPT-5.5, gated, recall + dream rollouts):
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SKILLOPT_SLEEP_WORKERS=24 PYTHONPATH=. $PY -m skillopt_sleep.experiments.run_nightly \
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--backend azure-responses --model gpt-5.5 --benchmarks searchqa --gate on \
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--replay-mode retrieval --retrieve-k 20 --rollouts 5 --nights 5 --per-night 10 --json
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# full grid across models/benchmarks/modes:
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SKILLOPT_SLEEP_WORKERS=32 PYTHONPATH=. $PY -m skillopt_sleep.experiments.run_nightly_matrix \
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--model gpt-5.5 --replay-mode retrieval --retrieve-k 20 --nights 5 --per-night 10 --rollouts 5
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```
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Back to the module overview: [`docs/sleep/README.md`](README.md) ·
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full reference: [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
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