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Adaption Labs introduces 'Invent a Dataset': training data generated from a task description, not a seed corpus. September 4, 2026 This week, Adaption Labs released Invent a Dataset. The feature generates a structured, training-ready dataset from a description of the behavior you want a model to learn. You do not arrive with a seed corpus, a predefined schema, or a labeling guide. Is it deployable? Yes, with one caveat. Invent a Dataset is live now in the Adaption app and through the Python SDK and REST API. Generated rows download as JSONL, JSON, CSV, or Parquet, so the artifact is a portable file you own and can train on anywhere. Generation itself runs on Adaption's hosted platform and consumes credits. No self-hosted generation path is documented. The problem being targeted. Most dataset workflows begin with data that already exists. Teams then spend weeks labeling, filtering, and reshaping it to approximate the target task. Adaption's argument is that this caps model quality at how closely the available data matches the intended behavior. For proprietary and specialized tasks, the relevant signal usually sits in internal systems, unstructured text, or workflow logs. It rarely converts cleanly into a focused training set. The research team also draws a line against existing synthetic-data tooling. Those tools automate generation after a human has already defined the schema, task distribution, and generation strategy. Invent a Dataset starts one level earlier, at the behavior itself. How the API works. The mechanics are documented and concrete. A single call to datasets.invent creates the dataset and starts generation, returning immediately with status running. You then poll datasets.get until the status reads succeeded or failed, and download the rows. Domain codes are the primary control. You fetch current codes with datasets.invent_domains rather than hardcoding them. You then pass values such as medical, optionally narrowed by qualified subdomain codes such as medical.symptoms_diagnosis. At least one domain or subdomain is required. Multiple domains contribute to the same run. A domain passed without subdomains draws from its full scope. Two output formats are supported. instruction_dataset is the default and produces prompt-completion pairs for supervised fine-tuning. preference_pairs produces chosen and rejected completions for preference-based training such as DPO. Three parameters matter for production use. estimate=True prices the exact request and returns estimated versus available credits without creating or charging anything. prompt accepts up to 10,000 characters to steer what the rows are actually about. idempotency_key accepts up to 255 characters and makes network retries safe by returning the original dataset instead of launching a second run. Row counts are subject to a per-launch limit set by your plan. Language and locale expansion. language_expansion runs in two modes. translate produces a new row variant for each target language. localize produces a variant for each country and language pair, using locale-specific wording rather than direct translation. A sample_rate between 0.01 and 1 controls what fraction of invented rows gets expanded, and credits are billed on the expanded output row count, not the original. Unsupported codes return a 400 with a sample of valid values. The zero-data loop. Invent a Dataset is the first half of a loop. The dataset ID passes directly to autoscientist.create, which co-optimizes the data and the training recipe against your objective. AutoScientist launched in May 2026 and is the training-side counterpart to the Adaptive Data pillar. Adaption reports that AutoScientist beats training configured by its own research staff, by an average of 35%. Win rates moved from 48% to 64%. Those figures come from in-house domain-specialized evaluations across eight verticals. Dataset sizes ranged from 5,000 to 100,000 rows, on architectures offered for fine-tuning by Together AI. Key takeaways. * Invent a Dataset generates training rows from a task description, with no seed corpus, schema, or labels. * One datasets.invent call sets domains, row count, format, and language expansion; generation is async. * Output is instruction pairs or preference pairs, downloadable as JSONL, JSON, CSV, or Parquet. * Dataset IDs feed straight into AutoScientist, closing an intent-to-trained-model loop. Need to partner with Marktechpost LLC. for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with Marktechpost LLC. Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.

Adaption and AI Singapore pilot to improve model training efficiency in Southeast Asia. Aug 04, 2026 Why frontier AI models underperform in Southeast Asian languages. Southeast Asia is home to more than 700 million people, over 1,200 languages, and 11 countries. Frontier language models, trained overwhelmingly on Western, English-centric data, perform inconsistently on Southeast Asian languages and often miss the region's cultural context. Adaption collaborated with AI Singapore (AISG) to enhance dataset quality and expand training dataset size via localization across five low-resource Southeast Asian languages. Adaption's AutoScientist was able to save 56 engineering hours' worth of manual optimization of model training hyperparameters for AISG. AISG is a national initiative driven by the Ministry of Digital Development and Information (MDDI), the Infocomm Media Development Authority (IMDA), and the National Research Foundation, Singapore (NRF). AISG brings together Singapore-based research institutions and the broader ecosystem of AI start-ups and companies to support use-inspired research, grow knowledge, create tools, and develop the talent to power Singapore's AI efforts. Among AISG's flagship products is SEA-LION (Southeast Asian Languages In One Network), Southeast Asia's first family of open-source large language models, built to understand the region's languages, cultures, and context. SEA-LION is trained to cover 11 national Southeast Asian languages and a number of major regional dialects. Since its first release in December 2023, SEA-LION has seen over 900,000 model downloads and more than 6.5 million API calls, across an ecosystem spanning over 70 community partners. Frontier language models, trained overwhelmingly on Western, English-centric data, perform inconsistently on Southeast Asian languages and often miss the region's cultural context. Targeting this gap means AISG's team has to address two connected problems at once: the quality and coverage of the training data, and the speed of the cycle that turns that data into an evaluated model. The data problem. Southeast Asian training data is scarce relative to demand, quality is inconsistent across sources, and much of what exists misses the local dialects, nuance, and cultural context that separate a merely multilingual model from a genuinely regional one. The training problem. Techniques proven in English text don't automatically transfer to Southeast Asian languages, and the field moves fast enough that confirming what works, and what needs adjusting, is a constant race against the clock. How Adaptive Data expanded SEA-LION's training set to 1.75 million samples. Adaption partnered with AISG for a pilot using Adaptive Data, Adaption's data enhancement and localization engine, to overcome both gaps at once: raise the quality of existing training data, and extend coverage into low-resource SEA languages, on a timeline that a fully manual approach couldn't match. Based on a sample of 1,050,000 rows from AISG's SEA-Instruct-2602, spanning domains including writing, math, science, and education, across seven Southeast Asian languages, Adaption's data engine delivered the following within one month: * Enhanced the 1 million data samples, applying Adaptive Data to improve the overall quality score through reasoning traces in the language of the sample. * Generated approximately 750,000 new samples by further localizing the existing 1 million samples, adding additional native-quality coverage in five additional languages and regional variants: Singapore Tamil, Singapore Malay, Burmese, Khmer, and Lao. What data enhancement changes in a Thai training sample. Enhancement here means more than cleanup. Take a Thai prompt asking for "10 words describing the benefits of renewable energy." The instruction is ambiguous in the original: it could mean a single ten-word sentence or ten separate sentences, and the original completion resolves it with one vague line. The enhanced version specifies the structure explicitly, a numbered list of ten sentences in Thai, and the completion delivers ten distinct, substantive points. Ambiguous quantity instructions produce unpredictable outputs. Fixing them creates consistent, measurable training signals. Why localizing training data is not the same as translating it. Localization goes further because moving a sample between languages is not translation. A Thai prompt asking for an asteroid joke relies on Thai wordplay that has no equivalent in Tamil. Rather than translating the joke and losing it, Adaptive Data reoriented the sample to Singapore Tamil and produced a two-asteroid dialogue in colloquial register, using the natural spoken markers of Singapore Tamil casual speech rather than textbook Tamil. The result teaches the model to adapt creative tasks to cultural and linguistic context, not just to swap vocabulary. How long manual data localization takes by comparison. The scale of what Adaption automates becomes clearer against the manual alternative: localizing and validating training data by hand takes significant time and specialized expertise. For example, three evaluation sets would typically take two visiting scholars two full working weeks to localize and validate: SEA-IFEval (105 rows), SEA-MTBench (58 x 2 rows), and a 1,000-row translation set. With Adaptive Data, 750,000 localized samples across five languages were processed in under a month. What AutoScientist changes for enterprises. By automating the parts of model training that once required specialized engineering skills, Adaption's platform opens the research loop to more of AISG's team and shortens the path from idea to evaluated model. What the team no longer builds by hand: data pipelines, failure handling as datasets grow, and hyperparameter exploration. Adaptive Data enriches and localizes seed data automatically, while AutoScientist abstracts the experimentation that previously demanded specialized tuning expertise. Where the team focuses instead: the judgment calls that drive the science. AISG's team sources seed data, designs evaluation criteria, reviews intermediate and final outcomes, and adjusts the training recipe based on what they learn. What runs in the background: AutoScientist manages the infrastructure behind long-running training and evaluation jobs and adds data to mitigate issues like catastrophic forgetting or lack of diversity, without researchers monitoring each run. Build sovereign AI with Adaption. The pattern behind this project is not specific to national AI programs. It applies to any organization with proprietary data and requirements that general-purpose models don't meet, whether those requirements are a language, a regulatory environment, or a domain vocabulary. Adaption provides enterprises, early-stage startups, communities, and sovereign initiatives with ownership of their intelligence, building continual learning systems that let organizations shape, train, and own their AI. Three pillars make this possible: Adaptive Data for shaping training data, Adaptive Intelligence for models built for any industry or language, and Adaptive Interfaces for reimagining how people interact with AI. The full case study includes side-by-side samples of enhanced and localized data in Thai and Tamil, with commentary on why each change matters for dataset quality. Read the full case study.