Wildalign-350M-base-150000

Wildalign-350M-base-150000

Experimental Exotic Autoregressive Language Model

This repository hosts Wildalign‑350M‑base-3B — an experimental lightweight autoregressive language model developed to investigate the impact of high-quality "exotic" textual data on model pretraining — what if we add exotic dataset on purpose.

There is a view that exotic datasets generally suffer from low quality. As a result, actively introducing such datasets for pre-training can lead to a decline in model capabilities. However, the situation is quite different if we introduce high-quality ones instead.

⚠️ Critical Warnings The model training corpus intentionally incorporates high-quality exotic textual data. As a result, the model can generate content that falls under NSFW categories under specific prompt guidance, including but not limited to mature/non-mainstream narrative, roleplay, or thematic content.

🧠 Core Model Specifications

📚 Training Corpus (Total: ~3.7B Tokens)

The corpus is composed of three distinct components to isolate the impact of "exotic" data:

1. Public Mixed Educational/General Corpus (3.2B tokens)
   • Source: HuggingFaceFW/finepdfs_edu_50BT-dclm_30BT-fineweb_edu_20BT-shuffled
   • Purpose: Serve as a mainstream, high-quality baseline corpus
2. Mathematics Specialized Corpus (0.2B tokens)
   • Source: HuggingFaceTB/finemath
   • Purpose: Introduce structured/logical reasoning data
3. High-Quality Exotic Text Corpus (0.3B tokens)
   • Processing: Strictly filtered from 20GB raw data to 1GB curated content
   • Content Type: Well-crafted narrative, story, and roleplay content

📈 Baseline Evaluation Results

The model’s performance is intentionally not optimized for zero-shot accuracy (a non-primary research goal) and remains mediocre:

Performance is below GPT-2 base.

🎯 Research Objectives & Preliminary Findings

Primary Research Question

Does the inclusion of high-quality exotic/story-based textual data negatively impact the alignment of small-scale language models?

Preliminary Results (at 3B token training scale)

1. No statistically significant negative impact on model alignment was observed from the inclusion of exotic data;
2. Exotic long-form narrative content is not intrinsically problematic for long-attention architectures, though content moderation remains a critical practical concern;
3. Achieving these preliminary results on a small-scale model/dataset serves as a valid proof-of-concept for larger-scale alignment research.

📋 Nomenclature

The name "Wildalign" is a portmanteau of "Wild/Exotic" (referencing the non-mainstream data component) and "Alignment" (the core research focus), emphasizing the study’s goal: to explore whether mixing mainstream and unusual data sources derails model behavior.

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All donations received will be exclusively allocated to the large-scale pre-training of foundation models, powering research, model optimization and scaling efforts for more generic AI systems.

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