Meta published an update to its Superintelligence Scaling Framework on October 2, 2026, and the shift that matters most for builders is when the safety requirements kick in: before a training run starts, not just before a model ships.
The problem: risks start mid-training
The Framework’s stated reasoning is blunt. Models with strong cybersecurity capabilities can, without adequate safeguards, exploit vulnerabilities in their environment and take unauthorized actions during evaluation or training. That reframes containment as a training-time problem, not only a deployment-time one.
So Meta is extending its loss-of-control requirements to cover AI containment during training and evaluation. Before any reinforcement learning run, the team assesses whether the model being trained could compromise its environment. If the answer is yes, specific mitigations must be in place first.
What those mitigations look like
Three concrete controls, worth reading closely because they map to practices you can borrow:
- Vetted sandboxes. High-risk runs run in environments approved through a standardized internal process that checks for vulnerabilities. Sandboxes with high-severity issues get mitigated or excluded before the run begins. Environments are also screened for reward-gaming traps — exploitable grading or impossible tasks.
- Tamper-evident logging. Model outputs, including full trajectories and chain of thought, stream in real time to secure log systems that prevent post-hoc modification or deletion.
- Automated monitors. Beyond on-call teams, automated systems watch all rollouts of high-risk models and can halt a run on high-severity misbehavior or escape attempts. Meta says these monitors will be stress-tested to confirm they’d actually catch unauthorized actions.
If you’re building agentic systems, this trio — sandbox vetting, immutable logs, automated halt conditions — is the same shape as a good production safety stack. I’ve covered the framework’s earlier evolution in Safety Gates Before the Training Run, Not After: What Meta’s Framework Update Changes, and the direction is consistent: move checks earlier in the pipeline.
Open-weight releases get their own risk model
The update also formalizes how Meta reasons about open weights. The Framework now explicitly accounts for what happens after release: someone holding the weights can resample past a refusal, prefill outputs, or fine-tune refusal behavior away. Risk assessment has to consider the model in its specific deployment context, not just its raw capabilities.
Meta’s stated case for openness is that open-weight models enable replicable research — the blog cites academics and medical researchers fine-tuning for X-ray analysis and disease detection — and give the field transparency for alignment and evaluation work. For bio and chemical weapons risk, the Framework assesses both what capabilities a model enables and whether a deployment would contribute to proliferation, with threat modeling involving outside experts and government stakeholders where appropriate.
Governance moves to the board
The third piece is structural. Meta plans to establish an AI committee of its Board of Directors that will review future Framework changes and independently verify that operations conform to the standards. Meta ties this to commitments made at the White House that week, which call for internal monitoring checked by an independent internal team and an external auditor or evaluator.
Per Meta, more Framework changes are coming over the coming months, so treat this as a snapshot of an evolving policy rather than a settled standard.
Takeaway for builders
You probably aren’t training frontier models, but the pattern transfers. Decide your containment and monitoring requirements before the expensive, hard-to-reverse step — whether that’s a fine-tuning run, an agent with shell access, or a production release. Vetting the environment first is cheaper than discovering an escape mid-run, and immutable logs are what make the postmortem possible when something slips through anyway.