Pretraining draws from massive text archives like Common Crawl; posttraining lacks an equivalent task repository.
OPEN ATHENA CLAIMS POSTTRAINING DATA COMMONS DON'T EXIST
*The Open Athena explainer warns that the surge in agentic AI has hit a data wall. Without a shared pool of tasks, open‑source large language models face a bottleneck that could cement corporate dominance.*
Open Athena’s latest explainer drops a stark warning: the era of agentic AI has outgrown the data foundations that powered the LLM boom. While the pretraining wave rode on a freely accessible ocean of text—Wikipedia, Stack Exchange, ArXiv, Project Gutenberg, and a 10‑petabyte Common Crawl archive—posttraining demands something entirely different: a commons of tasks. The blog argues that no such repository exists, and that the absence threatens the viability of truly open large language models. It points out that modern open models like Nemotron still lean heavily on the pretraining commons, underscoring how fragile the ecosystem is without a parallel task‑based supply chain.
Why is there no posttraining data commons?
Pretraining thrived on a massive, publicly available text corpus. Over the past 50 years humanity digitized encyclopedias, software code, Q&A archives, scientific papers and billions of web pages. Common Crawl alone has archived about 10 petabytes since 2007, and GPT‑3 drew roughly 60% of its training material from that source. The original Llama mix was almost entirely internet‑derived. Posttraining, however, consumes tasks, not static documents. Tasks are momentary, situated actions that disappear once performed. The blog stresses that no equivalent, ready‑made trove of tasks exists, leaving open‑source developers without the raw material needed for reinforcement‑learning fine‑tuning.
How does software become the de facto posttraining commons?
Software engineering has spent decades turning work into durable artifacts. Version control logs program history; continuous integration records test procedures; containers snapshot execution environments. These layers preserve a world, an intent, and a toolset—exactly the three components required for a reinforcement‑learning (RL) task. The article notes that computers’ deterministic nature lets an RL agent reset to a clean slate after each trial, making software an ideal substrate for task‑based learning. Yet even software is not immutable: system clocks drift, dependencies change, and bits rot. Debian’s reproducible‑build effort, started in 2013, still cannot guarantee full reproducibility after more than a decade.
Which open models still rely on the pretraining commons?
Open‑source LLMs such as Nemotron and Open Athena’s own models continue to draw the bulk of their pretraining data from the internet commons. The blog cites GPT‑3’s 60% Common Crawl share and the original Llama model’s near‑total reliance on internet‑derived text. It also mentions that incumbent AI players have shifted to custom crawlers, but the open community remains dependent on the publicly available Common Crawl archive for the vast majority of its pretraining material.
What limits the creation of a posttraining task pool?
A usable RL task must encode a world, an intent, and interaction tools, plus a grader to assess outcomes. Human tasks are fluid: goals shift, environments change, and the act itself is non‑repeatable. The article likens this to oral storytelling, where each retelling differs. Capturing that dynamism in a static dataset is fundamentally hard. Moreover, software artifacts that could serve as proxies suffer from version drift and bit rot, as illustrated by Debian’s ongoing reproducibility challenges. Without a systematic way to preserve and share these transient task structures, the posttraining frontier remains under‑served.
Frequently asked questions
What is the difference between pretraining and posttraining data?
Pretraining consumes static documents such as web pages and papers, while posttraining consumes tasks that represent situated actions and goals.
How much data does Common Crawl archive?
Common Crawl has archived about 10 petabytes of web data since it began regular snapshots in 2007.
Which models relied on Common Crawl for pretraining?
GPT‑3 was trained on roughly 60% Common Crawl‑derived data, and the original Llama model used an almost entirely internet‑sourced mix.
If the posttraining data gap persists, open‑source AI will increasingly rely on the private crawlers and task generators of industry giants. The blog’s analysis suggests that without a coordinated effort to capture, curate, and share task data, the open AI community may lose its ability to compete on the next frontier of agentic systems. Building a durable, reproducible task commons will be the next litmus test for AI democratization.
Sources: Hacker News – There is no posttraining data commons (https://openathena.ai/blog/there-is-no-posttraining-data-commons/)