How it works.

Actuator sits underneath every stage of post-training — fine-tune, align, distill, compress — and runs a closed-loop four-step cycle on each: monitor protected metrics, adjust pressure when they drift, guardrail against regressions, and certify the run with a full audit trail.

Fine-tune

↑↓

Align

↑↓

Distill

↑↓

Compress

↑↓

Actuator Control Layer

Monitor→ Adjust→ Guardrail→ Certify

↔ continuous closed-loop feedback

Multiple patents pending

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Better control, better models.

Today's post-training stack is a fragmented, open-loop affair. Teams pull together multiple tools, set knobs, run blind, eval after, and repeat. Actuator replaces that process with continuous live monitoring, automatic training-time adjustments, and guardrails to keep your model transformations on track. Quality in, quality out.

Safety & Alignment

+56.9pp

Worst-group accuracy @ 90% sparsity

Actuator stays unbiased while post-hoc methods collapse under aggressive compression.

Actuator

Post-hoc CDA

Post-hoc ERM

CivilComments Fairness — DistilBERT

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Robustness @ Compression

+13.1pp

Math reasoning vs. Wanda @ 50% sparsity

Co-scheduled compression recovers more quality across six benchmarks (GSM8K, MMLU shown).

GSM8K (Math Reasoning)

Actuator

39.0%

Wanda

25.9%

Post-hoc LoRA

12.8%

MMLU

Actuator

51.7%

Wanda

48.5%

Post-hoc LoRA

46.6%

Pruning — Llama-3.2-3B

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Serving Speed & Latency

+7.8%

Acceptance rate vs. pure distillation

Actuator-discovered teacher guidances outperform pure distillation and even hand-crafted rules.

Draft Model Acceptance Rate

Actuator (aligned)

25.9%

Actuator (curated)

24.3%

Baseline

24.0%

Distillation to 0.9B — DeepSeek-Coder-6.7B

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Reasoning & Capability

+15.8pp

Alignment preservation vs. DPO alone

Closed-loop stability prevents 2.3× more capability drift while maintaining reinforcement learning.

GSM8K (Capability Preserved)

Actuator

43.3%

DPO

27.5%

TruthfulQA (Output Quality)

Actuator

56.9%

DPO

56.4%

UltraFeedback RL — Qwen2.5-3B

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Plug and play.

Actuator makes post-training easy. It drops right in to your existing stack and provides the unified end-to-end software layer you need to ship better models while skipping the pain.

import actuator

 

run = actuator.Controller(

    protect = {"safety": 0.95, "math": 0.70},

    on_drift = "adjust", # auto adapt pressure

    on_breach = "halt", # stop and rollback

    certify = True, # produce audit trail

)

 

run.start(model) # any framework

Illustrative integration

Get early access.

Actuator is currently in closed testing with our early design partners. If your team is running serious post-training and want to do it a better way, please reach out! We're excited to hear about what your team is working on and open to potential pilots or partnerships.

[email protected]