OpenNovelty.org

Procedural Pretraining: Warming Up Language Models with Abstract Data

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
language modelspretrainingsynthetic procedurally-generated dataalgorithmic reasoning
Abstract:

Pretraining on rich web-scale corpora is the de facto paradigm for building language models. We study an alternative setting where the model is initially exposed to abstract structured data, as a means to ease the subsequent acquisition of semantic knowledge, much like mastering logic and mathematics for humans can support higher reasoning. We specifically focus on procedural data generated by formal languages and other simple algorithms.

Method and findings. We first use small models to identify algorithmic skills that different forms of procedural data can improve, often significantly. For example, on a diagnostic task for context recall (Needle-in-a-haystack), the accuracy jumps from 10 to 98% when pretraining on Dyck sequences (balanced brackets).

Second, we study how these gains transfer from abstract to semantic domains in larger models. We find that procedural pretraining significantly improves performance on natural language, code, and informal mathematics (C4, CodeParrot, and DeepMind-Math datasets), using as little as 0.1% extra procedural data. Notably, procedural pretraining also enables models to reach the same loss value with only 55, 67, 86% of the original data of these datasets.

Third, we explore the mechanisms behind these effects. We find that procedural pretraining instils non-trivial structure in both attention and MLP layers, and that the former is particularly important for code datasets, the latter for language. We also lay a path for combining the benefits of different forms of procedural data.

Implications. Procedural pretraining is a remarkably simple means of improving performance and speeding up training for transformers. It ultimately suggests the possibility of disentangling the acquisition of knowledge from reasoning in LLMs.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a procedural-first pretraining paradigm where language models are exposed to abstract algorithmic data (e.g., Dyck sequences, formal languages) before semantic corpora. It sits in the 'Synthetic Procedural Data for Skill Acquisition' leaf, which contains only one sibling paper among the thirteen total papers in the taxonomy. This indicates a relatively sparse research direction within the broader field of procedural and algorithmic pretraining, suggesting the specific sequencing of procedural-before-semantic training is not yet densely explored.

The taxonomy reveals neighboring work in procedural knowledge extraction from pretrained models and program trace-based representations, both within the same parent branch. Adjacent branches explore semantic representation integration (graph-based methods, neuro-symbolic planning) and multimodal procedural learning. The paper diverges from these by focusing on initial pretraining curriculum design rather than post-hoc reasoning augmentation or semantic grounding. The scope notes clarify that semantic-first pretraining and prompting-based approaches are explicitly excluded from this leaf, positioning the work at the intersection of curriculum learning and abstract skill acquisition.

Among thirty candidates examined, the empirical transfer demonstration (Contribution 2) shows one refutable candidate, while the procedural pretraining paradigm (Contribution 1) and mechanistic analysis (Contribution 3) each examined ten candidates with no clear refutations. This suggests the core paradigm and analysis methods appear relatively novel within the limited search scope, whereas the empirical transfer benefits have at least one overlapping prior work. The statistics indicate moderate prior work density for transfer claims but sparser coverage for the paradigm itself and its mechanistic underpinnings.

Based on the top-thirty semantic matches and taxonomy structure, the work appears to occupy a less-crowded niche within procedural pretraining research. The limited sibling count and sparse refutation statistics suggest novelty in the specific procedural-before-semantic sequencing, though the analysis does not cover exhaustive literature or adjacent fields like curriculum learning more broadly. The single refutable pair for transfer benefits warrants closer examination of overlap scope and claims.

Taxonomy

Core-task Taxonomy Papers
13
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Pretraining language models with abstract procedural data before semantic data. The field explores how language models can benefit from exposure to structured, procedural information prior to or alongside traditional semantic pretraining. The taxonomy organizes this landscape into several main branches. Procedural and Algorithmic Pretraining for Language Models focuses on synthetic data generation and skill acquisition through algorithmic tasks, often leveraging code-like structures or modular reasoning patterns. Semantic Representation Integration and Reasoning examines how models incorporate and manipulate meaning representations such as abstract meaning graphs or goal-grounded semantics. Multimodal and Domain-Specific Procedural Learning extends procedural reasoning to video understanding and specialized domains. Theoretical Foundations of Abstract Semantics provides the conceptual underpinnings for how abstraction and procedural knowledge relate to meaning. Representative works include Modular Algorithmic Structures[7], which emphasizes compositional reasoning, and Semantic Representations LLMs[3], which bridges symbolic and neural approaches. A particularly active line of work investigates whether synthetic procedural tasks can instill generalizable reasoning capabilities before models encounter natural language semantics. Procedural Pretraining[0] sits squarely within this synthetic procedural skill acquisition cluster, proposing that abstract algorithmic data can serve as a curriculum stage prior to semantic exposure. This approach contrasts with efforts like Procedural Knowledge Reasoning[4], which focuses on extracting procedural knowledge from existing semantic corpora, and Neuro-symbolic Planning[2], which integrates symbolic planning modules into neural architectures. Nearby, Modular Algorithmic Structures[7] shares the emphasis on compositional, algorithm-inspired pretraining but explores different modular decompositions. The central open question across these branches is whether procedural abstraction genuinely transfers to semantic understanding or whether it remains a complementary but separate skill, with ongoing debate about the optimal sequencing and integration of procedural versus semantic training regimes.

Claimed Contributions

Procedural pretraining paradigm for language models

The authors propose a new training paradigm where language models are first pretrained on abstract procedural data (generated by formal languages and simple algorithms) before standard pretraining on semantic data. This approach aims to teach elementary operations that facilitate subsequent knowledge acquisition.

10 retrieved papers
Empirical demonstration of transfer benefits across multiple domains

The authors demonstrate that procedural pretraining improves performance on diverse semantic domains including natural language, code, and informal mathematics. They show this works with minimal additional data and enables models to reach equivalent performance with substantially less standard training data.

10 retrieved papers
Can Refute
Analysis of mechanisms and localization of pretrained information

The authors analyze where useful pretrained information resides in the model architecture, finding that attention layers are more important for structured domains like code while MLP layers primarily help with natural language. They also explore combining benefits from different procedural data types.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Procedural pretraining paradigm for language models

The authors propose a new training paradigm where language models are first pretrained on abstract procedural data (generated by formal languages and simple algorithms) before standard pretraining on semantic data. This approach aims to teach elementary operations that facilitate subsequent knowledge acquisition.

Contribution

Empirical demonstration of transfer benefits across multiple domains

The authors demonstrate that procedural pretraining improves performance on diverse semantic domains including natural language, code, and informal mathematics. They show this works with minimal additional data and enables models to reach equivalent performance with substantially less standard training data.

Contribution

Analysis of mechanisms and localization of pretrained information

The authors analyze where useful pretrained information resides in the model architecture, finding that attention layers are more important for structured domains like code while MLP layers primarily help with natural language. They also explore combining benefits from different procedural data types.

Table of Contents