Granite 4.2 LLMs: How They're Built

A technical walkthrough of how we built the Granite 4.2 reasoning model family.

Authors: Granite Team, IBM

TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models. Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement learning pipeline. That pipeline includes agentic RL, where the 8B and 30B models learn to act with tools inside real sandboxed environments. Every model has a thinking / non-thinking switch, a low-effort thinking mode that spends a short reasoning budget on easy questions, and native tool calling. All Granite 4.2 models are released under the Apache 2.0 license.

Links:

• Granite 4.2 HF Collection
• GitHub Repository
• Granite Docs

Overview

Granite 4.2 is the reasoning-focused release of the Granite language-model family. Earlier Granite releases were strong instruction-following assistants; Granite 4.2 adds explicit reasoning. Every model can produce a chain of thought before its answer and can run in thinking or non-thinking mode depending on how much deliberation a task needs. A low-effort mode falls between the two, spending a short reasoning budget on easy questions.

The rest of this post walks through the build: architecture, pre-training, supervised fine-tuning, the multi-stage RL pipeline, and results.

Model Architecture

Granite 4.2 models are built on a decoder-only dense transformer architecture with the following core components:

Pre-Training

The pre-training recipe closely follows the previous generation; for a detailed treatment of the data blend, phase schedule, and long-context extension, see the Granite 4.1 blog.