A taxonomy for next-generation reasoning models

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A taxonomy for next-generation reasoning models

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https://www.interconnects.ai/p/next-gen-reasonersOn Monday of this week we released RewardBench 2, Ai2’s next reward model evaluation and a project I’ve been personally invested in through its whole arc. Read more of my thoughts here.Tomorrow, I’ll be presenting a version of this post at the AI Engineer World’s Fair Reasoning & RL track. Come tomorrow and say hi if you’re around the next two days!The first generation of reasoning models brought us inference-time scaling and intrigue in seeing into what can be called the reasoning process of a language model.The second generation of reasoning models are going to bring us new types of agentic language modeling applications.The traits and abilities that are needed for agentic models are additive to the first generation, but not present by default. Some of the new abilities that are needed can be bootstrapped with clever prompting, but for the best results we need to be training our reasoning models directly to optimize for planning.In this post we explain four key aspects of current and next-generation reasoning models:* Skills: The ability to solve self-contained problems.* Calibration: The ability to understand the difficulty of a problem and not overthink.* Strategy: The ability to choose the right high level plan.* Abstraction: The ability to break down a strategy into solvable chunks.These are presented in the order that they should be solved to make a progressively more complete reasoning model for complex tasks. Skills then calibration then strategy then abstraction. The first two are native abilities of models on single inference passes when presented with a technical problem and the latter are skills that are needed to build effective agents.For grounding, recall the popular “time horizon progression” chart from METR:The models were saturating around GPT 4o in 2024. Unlocking reasoning skills provided the bump through Claude Sonnet 3.7 in 2025. Planning well will be the trait of models that make the leap from 1 to 4+ hours in 2026 and on.All of the excitement around reasoning models exploded when it was shown that scaling reinforcement learning with verifiable rewards (RLVR) enables the model to learn useful skills for solving a variety of downstream tasks. The first public confirmation of this was with DeepSeek R1, which showed how training time RL compute translates to performance.Intertwined with this is that the models will generate more tokens per response while discovering these skills. Within all reasoning models today the above abilities listed — skills, calibration, strategy, and abstraction — can be further tuned by the increase in token spend per component.This year every major AI laboratory has launched, or will launch, a reasoning model because these models are better at acquiring skills that let them solve the hardest problems at the frontier of AI — evaluations like Humanity’s Last Exam, MATH, AIME, LiveCodeBench, Aider Polyglot, etc. have all seen step changes in performance from the previous class of models. These skills are the foundation for all of the changes that are following in the industry. Much of current discussions on scaling training are around finding the right problems to let the models become more robust in a variety of scenarios.The mad rush for skill acquisition in these models has ballooned a second-order problem of the models overthinking for even easy problems. This emerges due to the deep coupling of RL training and the unlock of inference-time scaling. The ultimate goal is clearly that models scale inference-time compute on their own proportional to how hard the problem is. In the short term, when the rate of performance gain is so high, it makes sense to prioritize abilities over efficiency. As abilities saturate, performance and cost will be weighted more equally.Right now, calibration on problem difficulty is offloaded to the user in the form of model selectors between reasoners or traditional instruct models, reasoning on/off buttons, thinking budget forcing, and soon reasoning effort selectors. On the research side its been shown that the RL loss functions are flexible enough to enable length control more precisely — something that loss functions like instruction or preference tuning cannot handle. Similarly, the models trained as reasoners better express their confidence, which should soon be translated into mitigations of overthinking.Calibrating the difficulty of the problem to the effort of the solution will enable much more practical (and faster and enjoyable) solutions for end users and also just more profitable solutions. Calibration, even though a lower level trait of the models, isn’t as much of a crucial path to rolling out new use-cases with the models. For that, AI makers are going to turn to better planning abilities.For more on current research on calibration, click the following footnote.Before we go on to planning abilities, which are often discussed at length in...