reasoning models

The 2025-2026 landscape isn't 'bigger is better' anymore. What matters is knowing why small models and reasoning models exist, the test-time-compute tradeoff, and routing the right model to the right task. Here is the practitioner's view.

Reasoning models are the 2025-2026 frontier. The signal is understanding they are trained (largely via RL on verifiable rewards) to generate long internal reasoning, and that they scale along a second axis: spending more compute at inference.

GRPO drops the value model and estimates advantage from a group of samples per prompt. The signal is the exact mechanics, why the group baseline works, and the normalization traps that silently bias training.

Swap a learned reward model for a checker that returns right-or-wrong, and reward hacking largely vanishes. The signal is why verifiable rewards beat learned ones for math and code, and where they break.

Distillation is not a single method. The signal is knowing when to match logits versus train on generated text, why on-policy distillation beats off-policy, and how reasoning models are distilled.

Most candidates answer 'use the reasoning model for hard problems' and stop. The interviewer wants it framed as an eval and a budget problem: how you prove the extra thinking tokens paid off, and what you do when the model talks itself out of a correct answer.

When each request thinks for 30,000 tokens, serving swings from compute-bound prefill to memory-bound decode, and the KV cache turns into the resource you genuinely schedule. The levers that governed chat traffic stop being the ones that count.