Why did the frontier shift to test-time scaling in 2024–2025?
Because post-training had reached its structural limit. RLHF and fine-tuning shaped behavior, but they could not increase reasoning depth. Base models remained static, and alignment could only move performance up to the substrate’s ceiling.
To go further, labs explored a new question: What if models could compute more at inference time? Not just predict, but deliberate — running multiple internal reasoning steps before producing a final answer.
This marked the beginning of Deep Thinking, or System 2-style cognition at inference.
How does a reasoning chain work during test-time scaling?
Instead of producing an answer in a single forward pass, the model breaks the task into deliberate stages:
Think 1 Break down the problem and identify sub-components.
Think 2 Analyze each component, explore intermediate steps, test hypotheses.
Think 3 Synthesize the result, reconcile contradictions, and construct a coherent final answer.
Each step generates tokens — “thinking tokens” — used internally before the model speaks.
More steps mean more computation. More computation means deeper reasoning.
This transforms inference from a single-shot prediction into a mini-planning loop.
Instead of picking the most likely next token, the model navigates a reasoning space, similar to a search process. Test-time compute becomes a lever for intelligence.
This parallels how humans think: we pause, deliberate, check our logic, and only then answer.
Which innovations define Phase 3?
Four core mechanisms deliver the bulk of performance gains:
Chain-of-Thought
Explicit step-by-step reasoning prompts. Breaks tasks into structured stages.
Tree Search
The model explores multiple branches of reasoning instead of one linear path. Useful for math, code, logic, and planning.
Self-Verification
The model checks its own intermediate steps, catching errors before producing an answer.
Best-of-N
Generate multiple candidate answers, evaluate them, and return the strongest one. A statistical amplifier for correctness.
Together, these enable test-time scaling — the ability to trade inference cost for reasoning quality.
Why does test-time scaling create breakthrough performance?
Because the depth of reasoning is no longer constrained by the base model. You can:
run deeper chains
explore more branches
verify more steps
cross-check intermediate reasoning
expand computation for hard problems
The result is a performance curve with a second inflection point. Accuracy rises far beyond the “base model line,” especially on:
math
logic
coding
multi-step reasoning tasks
planning problems
Phase 3 marks the first time that reasoning can be scaled independently from model size.
Where does Phase 3 excel?
Two domains show the largest lift:
1. Math and Coding
Reasoning chains reduce cascading errors, enabling models to construct, test, and refine solutions.
2. Multi-step Logical Tasks
Tree search and best-of-N approaches give the model multiple shots at solving complex puzzles or structured problems.
Test-time scaling turns LLMs into deliberative problem-solvers instead of fast pattern matchers.
This constraint cannot be solved by chain-of-thought alone. It requires changes to architecture, not prompting — setting the stage for Phase 4.
Why was Phase 3 essential for the evolution of AI agents?
Phase 3 revealed that intelligence is not just:
scale of parameters
volume of data
quality of alignment
It also depends on how the model allocates compute during inference. This insight unlocked the concept of agents with adaptive reasoning depth: thinking more when needed, conserving compute when tasks are simple.
Test-time compute became a dynamic resource — a step toward continuous cognitive activity.
Phase 3 provided the missing link between refined behavior and persistent state: the ability to generate deep thought on demand.
Final Synthesis
Phase 3 marks the era where inference became active computation rather than passive prediction. Deep thinking, multi-step reasoning, tree search, and self-verification created a second scaling curve above the limits of post-training. Yet all gains were bound by the context window’s finite size, revealing the need for memory-driven coherence. This constraint directly catalyzed the arrival of Phase 4.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
