On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models

2025-12-08

AI Summary

This work investigates the synergistic effects of pretraining, mid-training, and reinforcement learning (RL) post-training on language models’ reasoning capabilities—specifically whether RL genuinely extends the capability boundaries established during pretraining. Method: We introduce a controlled experimental framework grounded in synthetic reasoning tasks, atomic operation modeling, and interpretable reasoning-chain generation. Training distributions are systematically manipulated, and process-level rewards are incorporated to guide intermediate reasoning steps. Contributions/Results: (1) RL yields genuine reasoning improvements only when pretraining preserves sufficient capacity *and* RL focuses on boundary tasks; (2) mid-training substantially outperforms pure RL under fixed compute budgets; (3) context-based transfer requires only minimal pretraining; (4) process-level rewards significantly enhance reasoning fidelity. Collectively, these findings provide causal evidence and principled design guidelines for reasoning-oriented language model training.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training. A central challenge is the lack of control in modern training pipelines: large-scale pre-training corpora are opaque, mid-training is often underexamined, and RL objectives interact with unknown prior knowledge in complex ways. To resolve this ambiguity, we develop a fully controlled experimental framework that isolates the causal contributions of pre-training, mid-training, and RL-based post-training. Our approach employs synthetic reasoning tasks with explicit atomic operations, parseable step-by-step reasoning traces, and systematic manipulation of training distributions. We evaluate models along two axes: extrapolative generalization to more complex compositions and contextual generalization across surface contexts. Using this framework, we reconcile competing views on RL's effectiveness. We show that: 1) RL produces true capability gains (pass@128) only when pre-training leaves sufficient headroom and when RL data target the model's edge of competence, tasks at the boundary that are difficult but not yet out of reach. 2) Contextual generalization requires minimal yet sufficient pre-training exposure, after which RL can reliably transfer. 3) Mid-training significantly enhances performance under fixed compute compared with RL only, demonstrating its central but underexplored role in training pipelines. 4) Process-level rewards reduce reward hacking and improve reasoning fidelity. Together, these results clarify the interplay between pre-training, mid-training, and RL, offering a foundation for understanding and improving reasoning LM training strategies.

Problem

• Clarify RL's role in enhancing reasoning beyond pre-training
• Isolate causal effects of pre-training, mid-training, and RL
• Evaluate generalization in reasoning across complexity and context

Innovation

• Controlled framework isolates pre-training, mid-training, RL contributions
• Synthetic tasks with atomic operations and reasoning traces for evaluation
• Process-level rewards reduce hacking and improve reasoning fidelity