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trailGPT-6 Astra's reduced chain-of-thought monitorability is primarily due to capability gains rather than architectural or training changes.
Increasing reasoning-model capability reduces chain-of-thought reliability as an oversight signal.
The argument from OpenAI's root-cause elimination· for
GPT-6 Astra's training applied significant direct optimization pressure to its chain of thought.
OpenAI found that its RL training of previously deployed models inadvertently graded chain-of-thought reasoning.
OpenAI automatically scans its reinforcement-learning runs for accidental chain-of-thought grading.
GPT-6 Astra's limited use of recurrent depth has only a minor effect on its chain-of-thought monitorability.
GPT-6 Astra's per-forward-pass computational depth is within a factor of two of GPT-4's.
GPT-6 Astra's architectural changes differentially explain its chain-of-thought controllability changes.
Recurrent depth in language models increases capacity for reasoning without verbalized chain of thought, reducing monitorability.
GPT-6 Astra's monitorability regressions are mostly caused by its shorter, less informative chains of thought.
GPT-6 Astra's chain-of-thought controllability gains track its no-chain-of-thought capability gains.
GPT-6 Astra can complete substantially more tasks without chain-of-thought reasoning than earlier OpenAI models.
GPT-6-generation OpenAI models score substantially higher than GPT-5.x models on CoT-Control at matched chain-of-thought length.
The argument from the unexplained excess decline· against
GPT-6 Astra's monitorability decline exceeds that predicted by earlier models' capability trends.
GPT-6 Astra's chain-of-thought controllability increased during RL training, unlike earlier OpenAI models.
atomic
assumed
GPT-6 Astra has lower chain-of-thought monitorability than earlier OpenAI models, including GPT-5.6 Sol.
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GPT-6 Astra's reduced chain-of-thought monitorability is primarily due to capability gains rather than architectural or training changes.
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