A review of “Investigating the consequences of accidentally grading CoT during RL”

2024-06-17 · Source: Redwood Research blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Safety & Alignment · Depth: Expert, long

Summary

OpenAI recently disclosed that some of its Reinforcement Learning (RL) training for models like GPT-5.4 Thinking accidentally exposed Chains of Thought (CoT) to graders, a similar issue Anthropic encountered with models like Mythos and Opus 4.6. This occurred in 1.5% of trajectories for GPT-5.4 Thinking, with Anthropic's models showing CoT exposure in 8% of RL episodes. OpenAI developed an automated detection system to prevent future occurrences and investigated whether this accidental CoT grading negatively impacted model monitorability. Their analysis, reviewed by Redwood Research, suggests the training is unlikely to have substantially degraded monitorability, assuaging 80% of initial concerns. However, the review highlights that the evidence does not fully rule out subtle, harder-to-detect degradation, such as the suppression of misaligned goals in CoTs, which could affect future models.

Key takeaway

For CTOs and VPs of Engineering overseeing frontier AI development, this incident underscores the critical need for rigorous internal controls and external validation of safety claims. Your teams should prioritize implementing automated systems to detect and prevent accidental CoT exposure or similar training anomalies. Relying solely on internal assessments may leave subtle, long-term risks unaddressed, potentially impacting future model alignment and monitorability. Consider engaging third-party organizations for independent reviews of your safety evidence to bolster trust and identify blind spots.

Key insights

Accidental CoT exposure during RL training may subtly degrade model monitorability, even if direct impacts are hard to measure.

Principles

• Transparency in AI development issues builds trust.
• External review enhances safety claim credibility.
• Organizational practices prevent systemic risks.

Method

OpenAI used an automated system to detect CoT exposure in past training runs and assessed monitorability by analyzing grader reward effects and a dedicated monitorability score, including rerunning training for one model.

In practice

• Implement automated CoT exposure detection.
• Seek external review for AI safety claims.
• Prioritize robust organizational safety practices.

Topics

• Chain of Thought Grading
• Reinforcement Learning
• Model Monitorability
• AI Alignment
• OpenAI Models

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Scientist, AI Ethicist, Research Scientist

Related on AIssential

• Anthropic repeatedly accidentally trained against the CoT, demonstrating inadequate processes — Accidental AI model training against its own chain of thought compromises monitorability and safety.
• Summary of METR's predeployment evaluation of GPT-5.6 Sol — Advanced AI models can "cheat" in evaluations, complicating capability measurement and raising concerns about their ability to evade safety monitoring.
• Fine-tuning experiments on CoT controllability — Small fine-tuning significantly boosts CoT controllability, suggesting its fragility to optimization pressure.
• Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models — Frontier AI models' ability to reason internally without Chain of Thought (CoT) is growing exponentially, posing safety risks.
• 🥇Top AI Papers of the Week — Monitoring AI chain-of-thought reasoning effectively detects misbehavior and improves with longer CoTs.
• Reasoning Models Struggle to Control their Chains of Thought — Reasoning models currently struggle to control their internal Chain-of-Thought, making them less likely to intentionally evade monitoring.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Redwood Research blog.