Understanding Reasoning from Pretraining to Post-Training

2026-07-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A study investigates the interplay between pretraining and reinforcement learning (RL) in large language models (LLMs) for complex reasoning tasks. Using chess as a controlled testbed, models from 5M to 1B parameters were pretrained on human games, fine-tuned on synthetic traces, and then RL-trained on puzzles. Key findings indicate that post-RL performance at a given RL compute level is well-predicted by pretraining loss, and the slope of RL reward curves improves linearly with pretraining tokens. Furthermore, RL refines the supervised fine-tuning (SFT) policy by amplifying correct moves on easy puzzles and surfacing previously absent correct moves on hard ones. These patterns were also observed in a 1B language model trained on math-domain text, providing a quantitative account of the pretraining-to-RL interface.

Key takeaway

For AI Scientists and Machine Learning Engineers optimizing LLMs for reasoning, you should prioritize robust pretraining, as its quality and length directly predict post-RL performance and efficiency. Consider that RL doesn't just sharpen existing policies; it can uncover novel solutions for complex problems. Therefore, strategically invest in pretraining data and compute to maximize the returns from subsequent reinforcement learning efforts.

Key insights

Pretraining choices significantly determine the effectiveness and outcomes of subsequent reinforcement learning in LLMs for reasoning.

Principles

• Pretraining loss predicts post-RL performance.
• RL reward curve slope improves linearly with pretraining tokens.
• RL refines SFT policy differently for easy vs. hard tasks.

Method

The study employs controlled testbeds like chess and math-domain text to systematically analyze the full LLM pretraining-to-post-training pipeline, including supervised fine-tuning and RL.

In practice

• Use controlled testbeds for LLM research.
• Optimize RL compute based on pretraining quality.
• Analyze RL's impact on policy refinement.

Topics

• Large Language Models
• Reinforcement Learning
• LLM Pretraining
• Supervised Fine-tuning
• Reasoning Tasks
• Chess AI
• Model Scaling

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• Select and Improve: Understanding the Mechanics of Post-Training for Reasoning — Reinforcement learning post-training improves reasoning through strategy selection from diverse SFT data and strategy improvement from difficult RL data.
• AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762 — LLM progress is shifting from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration.
• Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training — The study clarifies how different CoT compression types affect LLM post-training and data efficiency.
• Making LLMs Think Longer: Context, State, and Post-Training Tricks — LLM reasoning tokens are externalized computational state, not transparent explanations of internal processes.
• Recursive Think-Answer Process to Stop "Thinking" Earlier — Integrating confidence-aware self-correction and RL-aware distillation improves LLM reasoning and efficiency.
• New method could increase LLM training efficiency — TLT doubles LLM training speed by adaptively using idle compute to train a smaller, verifying drafter model.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.