Why are AI models getting better at reasoning, and why is that a problem?

In recent months, reasoning models have become the focus of most leading AI labs due to the significant improvements in solving logic-requiring problems through Chain of Thought (CoT) reasoning and reinforcement learning (RL) training. Training these reasoning models involves additional processes typical for standard large learning models (LLMs), and that is why they also require different methods of interpretability, such as neural verification and learning from verifiable rewards. Additionally, producing exemplar chain of thought outputs can become insurmountable for humans to produce, that is why we are seeing SoTA models like DeepSeek RL Zero forgo any supervised training and become completely dependent on large-scale training with RL. However, these methods are susceptible to reward hacking and other RL failure modes, more so than in standard LLMs because these CoT models have many chances to explore their reward landscape during training.

Most of the high-performing LLMs (e.g. Kimi-k1.5, DeepSeek-R1, o1, etc.) released between the end of 2024 and 2025 used long CoT models and RL within their fine tuning or alignment stages. Therefore, without robust interpretability and oversight, this can breed unchecked reasoning and extreme misalignment. Given that we are concomitantly seeing an increase in RL failure modes developing in large language models, there is an urgent need for better understanding of them and RL interpretability. After discussing and ideating on these problems, we wanted to make more technical people understand why reinforcement learning failure modes matter, and why we need research and improved tools for RL interpretability. We have decided to build an interactive resource that would act as a starter’s guide to understanding current research in reinforcement learning failure modes and interpretability.

Our major goal with this resource is to reduce the barrier to entry for newcomers to RL interpretability and also to raise awareness of the failure modes that can occur in RL agents if they are misaligned. People do not take AI alignment claims seriously if they do not understand risks at a certain technical level; otherwise they tend to think about claims regarding the ‘impeding ai apocalypse’ in strictly sci-fi terms and hence find those claims tenu...

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