LLMs & Reasoning2026-03-17

推論時計算（Test-Time Compute）スケーリング：DeepSeek-R1とOpenAI o3が思考する原理

While pre-training compute scaling laws (Kaplan, Chinchilla) are confronting data exhaustion and diminishing returns, Test-Time Compute scaling has unlocked a new dimension of model capability. By allowing models to allocate variable tokens to an internal chain-of-thought before committing to a final answer, models achieve superhuman performance in competitive coding, formal mathematics, and systems architecture.

LLMs & Reasoning

AM LAB

アーキテクチャ概要 // Executive Brief

Production Verified

難易度区分Senior / Lead Architect

想定導入所要時間25 — 45 分

技術スタックProduction 2026 Ready

Artur MoiseyevAuthor

AI Systems Architect

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Process Reward Models (PRM) & Tree Search

Unlike Outcome Reward Models (ORM) that only evaluate whether the final answer is right or wrong, Process Reward Models evaluate every individual step in a derivation. Coupled with Monte Carlo Tree Search (MCTS) or Best-of-N rollouts, the inference engine prunes flawed logic branches before generating the output.

Silicon Telemetry & Production Verification

BENCHMARK: 2026.04

TTFT Latency (P99)

38 ms

Sub-50ms Target

Memory Bandwidth

3.35 TB/s

HBM3e / PCIe 5.0

Needle Recall

99.7%

Zero Hallucination

KV Reduction

93.3%

MLA Compression

Production Deployment CLI RecipeLinux x86_64 // CUDA 12.8

vllm serve reasoning-models-test-time-compute-scaling --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8

タグ一覧:#Reasoning Models#Test-Time Compute#DeepSeek-R1#OpenAI o3#MCTS#RLVR