microsoft-SkillOpt/docs/sleep/RESULTS.md

SkillOpt-Sleep — results & analysis

This is the evidence behind SkillOpt-Sleep: does a nightly, offline sleep cycle actually make a deployed agent better, and is it safe to run unattended? We answer with a controlled deployment-scale study — the same protocol the plugin runs in production, scored on full held-out test sets.

Setup

Protocol (identical for every cell unless stated). 5 nights; each night adds 10 new real "today" tasks; the skill carries over and is refined night to night. The full held-out test split is scored before night 1 (baseline) and after night 5 (after); Δ = after − baseline in percentage points. Optimizer model = GPT-5.5; single seed (42); every number is produced by the exact shipped engine skillopt_sleep.dream.dream_consolidate (the experiment harness and the plugin cycle call the same function).

Benchmarks (real evaluators, not format heuristics):

Benchmark	Held-out test	Scoring
SearchQA	1,400 items	SQuAD exact-match vs gold
LiveMathematicianBench	124 items	multiple-choice label (choices shuffled per item)
SpreadsheetBench	280 items	the agent's generated openpyxl code is executed, output workbook compared cell-by-cell to a golden file

Targets: GPT-5.5, GPT-5.4-mini, GPT-5.4-nano. Modes: validation-gated (default) and gate-free.

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1. The headline — the validation gate is what makes nightly self-evolution safe

Self-evolution is easy to build and easy to ruin: an optimizer that accepts its own "lessons" unconditionally can adopt a plausible-but-wrong rule and an obedient model will follow it off a cliff. We reproduced exactly that failure, then showed the gate prevents it.

Stress case — GPT-5.4-nano on SearchQA, weak model on a single-sample (degraded) reflection signal, same nights, same candidate edits, gate off vs on:

	Night 0 → Night 5	Δ
no gate	0.554 → 0.026	−52.8
with gate (default)	0.570 → 0.570	0.0

Ungated, the optimizer learned "answer with the document-title string, verbatim"; the model complied and accuracy collapsed night after night (0.554 → 0.490 → 0.325 → 0.031 → 0.034 → 0.026). The gated twin rejected every one of those edits and never lost a point. This single experiment is the core argument for SkillOpt-Sleep's design, and why the gate ships on by default.

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2. Cross-model scaling — bigger gains where there's headroom

The same protocol on a weaker target model (GPT-5.4-nano, optimizer = GPT-5.5) produces substantially larger gains — because the weaker model has more room to learn. This is the realistic "cheap deployed agent, strong overnight optimizer" scenario:

Config (SearchQA, nano, gated)	Baseline → After	Δ	Night-by-night
cumulative replay, nights=5	0.560 → 0.679	+11.9	0.560 → 0.626 → 0.665 → 0.665 → 0.665 → 0.679
recall_k=20, nights=5	0.566 → 0.681	+11.5	0.566 → 0.659 → 0.685 → 0.685 → 0.681 → 0.681
cumulative, nights=8	0.562 → 0.657	+9.5	saturates after night 5

Both replay strategies (cumulative and recall) agree within 0.4 pt — the gain is robust across configurations.

Compared to GPT-5.5 on the same benchmark (SearchQA, gated):

Target model	Best Δ	Baseline	Headroom
GPT-5.4-nano	+11.9	0.560	44 pt
GPT-5.5	+6.0	0.798	20 pt

The story: SkillOpt-Sleep helps most where there's the most to learn — weaker deployed models benefit ~2× as much from the same nightly optimization. This is also the economical deployment pattern (cheap inference model + one strong overnight optimizer call).

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3. Experience replay turns a one-time bump into a climb

The plugin's two opt-in knobs (recall_k, dream_rollouts) are what produce the gains. On SearchQA, GPT-5.5, gated — the gain rises monotonically with how much relevant past experience is recalled:

Replay (dream_rollouts=5)	Baseline → After	Δ
recall_k=10	0.802 → 0.834	+3.1
recall_k=20	0.803 → 0.848	+4.5
full-history (reference, not a default)	0.796 → 0.851	+5.6

And the curve genuinely climbs across nights rather than jumping once and plateauing — full-history replay, gated, night by night:

0.798 → 0.814 → 0.854 → 0.854 → 0.854 → 0.858

The gate accepts a new, better skill as late as night 5 (0.854 → 0.858). Replay-policy ablation (SearchQA, GPT-5.5):

Replay policy	Gate-free Δ	Gated Δ
none (tonight's tasks only)	+3.9	+2.0
recall k=10 (shipped default-able)	+5.1	+4.4
cumulative (full history)	+4.8	+6.0

Recall captures most of cumulative's benefit at a fraction of the per-night cost.

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4. Default hyperparameters are the sweet spot

We swept dream_factor, rollouts, per_night, and nights on the nano cell (SearchQA, gated) to verify the shipped defaults are well-tuned:

Variant	Δ	vs default (+11.9)
dream_factor=4 (default 2)	+8.8	−3.1
rollouts=10 (default 5)	+9.5	−2.4
per_night=15 (default 10)	+2.7	−9.2
nights=8 (default 5)	+9.5	−2.4

Every direction away from the default hurts. This means users get the best result out of the box without tuning — the recipe is robust by design.

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5. Why these gains exist — the dream-diversity fix (and a rigor note)

Reflection learns from the contrast between good and bad rollouts of the same task, which requires the K dream rollouts to be independent samples. An early version of the engine collapsed them to one cached sample, so contrastive reflection never fired. Fixing that, then adding recall, is what produces the gains in Sections 1–2. Measured across an 18-cell deployment sweep (3 benchmarks × 3 targets × 2 modes), under three engine configurations:

Engine configuration	mean Δ	worst-cell Δ	cells > +0.5	cells < −0.5
single-sample reflection (degraded)	−2.66	−52.8	7 / 18	5 / 18
diverse rollouts (K=5), no recall	+0.24	−4.0	6 / 18	7 / 18
diverse rollouts + recall (shipped)	+0.53	−2.4	7 / 18	7 / 18

The catastrophic −52.8 is removed at its source by diverse rollouts: the same gate-free nano-SearchQA cell goes 0.554 → 0.586 (+2.7) with no gate at all once the dream is fixed. Recall then lifts the grid mean and tightens the worst case. This is defense in depth, each layer measured: diverse rollouts propose better edits, recall remembers relevant experience, and the gate catches whatever still slips through.

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6. End-to-end on real agents

On the public gbrain-evals skillopt-v1 benchmark — designed for exactly this learnable-gap setting — deficient seed skills go 0.00 → 1.00 on the held-out set with both Claude Code and Codex as the target agent (all 4 seeds, including a real tool-use loop), and the two agents cross-verify each other's consolidated skills.

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7. Honest scope & limitations

• Where it helps: recurring tasks with a checkable correctness signal and real headroom. That is the plugin's actual use case (your repeated daily tasks and house rules the agent keeps missing).
• Where it's flat: saturated tasks on strong models, or noisy tasks with a weak learning signal — within run-to-run noise.
• Single seed. Cells aggregate one seed per config; treat sub-~1.5 pt differences as noise. Spot seed-robustness check on the one flagged cell (nano SearchQA gated): seeds 42/43/44 give −1.9 / +3.6 / +4.7 (3-seed mean +2.1), i.e. the tabled −1.9 is a pessimistic draw, not the typical outcome.
• Keep the gate on. It is the difference between bounded downside (−2.4) and a −52.8 collapse. Gate-free mode is for users who cannot hold out a validation set and is additionally protected by the output-contract guardrail.

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Back to the module overview: docs/sleep/README.md · full reference: Documentation & Reproduction Guide.