ThumbGate Verification evidence
guide | reasoning compression guardrails

# Reasoning Compression Needs Step-Level Safety Checks

Efficient reasoning can reduce token cost, but short traces can destabilize accuracy. ThumbGate gates reasoning compression with verifier outcomes, pass@1 baselines, low-confidence step review, and high-confidence failure inspection.

👍 Thumbs up reinforces good behavior
👎 Thumbs down blocks repeated mistakes

## Why this page exists

- Reasoning compression is only ROI-positive when quality survives the token savings.
- Step-level confidence matters because correct final answers can still contain brittle intermediate steps.
- ThumbGate now exposes npx thumbgate reasoning-efficiency-guardrails for verifier-backed model routing and prompt-eval workflows.

## Why this helps ThumbGate

This creates a model-cost governance story: ThumbGate can help teams route cheaper, shorter reasoning only when a verifier proves the compressed trace is still safe.

The same mechanism also protects DPO/RLHF export and model-evaluation workflows from rewarding or punishing the wrong reasoning steps.

## High-ROI reasoning gates

- Require verifier and pass@1 evidence before compressing reasoning traces.
- Checkpoint low-confidence steps even in correct rollouts before reinforcing them.
- Checkpoint high-confidence failed rollouts for truncation or verifier noise before penalizing them.
- CLI path: npx thumbgate reasoning-efficiency-guardrails --baseline-tokens=1200 --compressed-tokens=980 --baseline-accuracy=0.84 --compressed-accuracy=0.85 --verifier --json.

## Where this creates revenue

This improves the Pro and Team model-hardening story. Buyers can save inference cost and evaluation time without turning "shorter reasoning" into an unmeasured reliability regression.

## FAQ

Should every agent use shorter reasoning traces?

No. Compress traces only when verifier outcomes and accuracy baselines prove the shorter route still works for the workload.

How does ThumbGate use step-level reasoning signals?

ThumbGate does not train the model directly. It gates routing, exports, and workflow decisions when low-confidence steps, high-confidence failures, or missing verifier evidence make compression risky.