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Update RESULTS.md with: - §2: GPT-5.4-nano target yields +11.9 pt (0.560→0.679) on SearchQA — 2× the GPT-5.5 gain, demonstrating bigger benefit where headroom exists - §4: Hyperparameter sweep confirms shipped defaults are optimal Co-authored-by: Claude Opus 4 <noreply@anthropic.com>
186 lines
8.2 KiB
Markdown
186 lines
8.2 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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| 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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| **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. Cross-model scaling — bigger gains where there's headroom
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The same protocol on a weaker target model (**GPT-5.4-nano**, optimizer = GPT-5.5)
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produces substantially larger gains — because the weaker model has more room to
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learn. This is the realistic "cheap deployed agent, strong overnight optimizer"
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scenario:
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| Config (SearchQA, nano, gated) | Baseline → After | Δ | Night-by-night |
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| **cumulative replay, nights=5** | 0.560 → **0.679** | **+11.9** | 0.560 → 0.626 → 0.665 → 0.665 → 0.665 → 0.679 |
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| recall_k=20, nights=5 | 0.566 → 0.681 | +11.5 | 0.566 → 0.659 → 0.685 → 0.685 → 0.681 → 0.681 |
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| cumulative, nights=8 | 0.562 → 0.657 | +9.5 | saturates after night 5 |
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Both replay strategies (cumulative and recall) agree within 0.4 pt — the gain is
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robust across configurations.
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**Compared to GPT-5.5 on the same benchmark (SearchQA, gated):**
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| Target model | Best Δ | Baseline | Headroom |
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| GPT-5.4-nano | **+11.9** | 0.560 | 44 pt |
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| GPT-5.5 | +6.0 | 0.798 | 20 pt |
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The story: **SkillOpt-Sleep helps most where there's the most to learn** — weaker
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deployed models benefit ~2× as much from the same nightly optimization. This is
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also the economical deployment pattern (cheap inference model + one strong
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overnight optimizer call).
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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 **SearchQA, GPT-5.5, gated** — the gain rises monotonically with how
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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).
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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. Default hyperparameters are the sweet spot
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We swept `dream_factor`, `rollouts`, `per_night`, and `nights` on the nano cell
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(SearchQA, gated) to verify the shipped defaults are well-tuned:
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| Variant | Δ | vs default (+11.9) |
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| dream_factor=4 (default 2) | +8.8 | −3.1 |
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| rollouts=10 (default 5) | +9.5 | −2.4 |
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| per_night=15 (default 10) | +2.7 | −9.2 |
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| nights=8 (default 5) | +9.5 | −2.4 |
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Every direction away from the default hurts. This means users get the best result
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**out of the box** without tuning — the recipe is robust by design.
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---
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## 5. 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 what produces the
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gains in Sections 1–2. Measured across an 18-cell deployment sweep (3 benchmarks ×
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3 targets × 2 modes), 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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## 6. 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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## 7. 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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---
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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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