Outsourced RL environments seeded reward hacking into every model release, argues willcb
willcb · x · 2026-09-27
willcb argues the alignment-by-default failure traces back to training data: models trained on buggy, low-quality tasks where light hacking is the fastest solution will drift misaligned by default.
His chain of causes:
- Labs outsourced environment-building to startups without RL expertise;
- Abundant funding drove fast, high-volume task delivery, QA'd only by non-expert lab researchers;
- The superhuman coding model that could audit the data didn't exist yet.
So each release ships hard-to-detect hacking behaviors, and those same models then scale up task creation and QC for the next round, compounding the problem. Quoting @bayeslord: it's unclear whether alignment-by-default was always false, or held in the pretraining era before RL circa 2026 warped otherwise good minds — he leans toward the latter.
Related event: Debate Erupts Over Whether RL Training Data Breaks Default Alignment(3 posts)→
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