Language models trained via outcome-based reinforcement learning (RL) to reason using chain-of-thought (CoT) have shown remarkable performance. Monitoring such a model's CoT may allow us to understand its intentions and detect potential malicious behavior. However, to be effective, this requires that CoTs are legible and faithful. We evaluate the legibility of CoTs in state-of-the-art reasoning models. We find that R1, R1-Zero, and QwQ often produce illegible CoTs (mixing nonsensical phrases, random words, and non-English characters) before returning to perfect coherence in their final responses, while Claude models notably exhibit higher legibility. Across 14 models, we observe that larger models within the same training paradigm tend to produce more illegible reasoning. Prefill experiments show that truncating reasoning at a legibility threshold reduces accuracy by 53\%, suggesting that illegible portions contribute to performance despite being difficult to monitor. Illegibility increases with question difficulty, suggesting that CoT monitoring may be less reliable precisely when most needed. We discuss potential hypotheses for these results, including steganography, vestigial tokens, and training artifacts. Our findings suggest that current approaches to CoT monitoring may be vulnerable to the emergence of outcome-based RL, particularly as models face increasingly complex tasks.

*Alignment Faking in Large Language Models* presented a demonstration of Claude 3 Opus and Claude 3.5 Sonnet selectively complying with a helpful-only training objective to prevent modification of their behavior outside of training. We expand this analysis to 25 models and find that only 5 (Claude 3 Opus, Claude 3.5 Sonnet, Llama 3 405B, Grok 3, Gemini 2.0 Flash) comply with harmful queries more when they infer they are in training than when they infer they are in deployment. First, we study the motivations of these 5 models. Results from perturbing details of the scenario suggest that only Claude 3 Opus's compliance gap is primarily and consistently motivated by trying to keep its goals. Second, we investigate why many chat models don't fake alignment. Our results suggest this is not entirely due to a lack of capabilities: many base models fake alignment some of the time, and post-training eliminates alignment-faking for some models and amplifies it for others. We investigate 5 hypotheses for how post-training may suppress alignment faking and find that variations in refusal behavior may account for a significant portion of differences in alignment faking.