TAI #125: Training Compute Scaling Saturating As Orion, Gemini 2.0, Grok 3, and Llama 4 Approach?
Author(s): Towards AI Editorial Team
Originally published on Towards AI.
What happened this week in AI by Louie
This week, the potential plateauing of LLM training scaling laws has been a focus of debate in the AI community. The Information reported that OpenAIβs scaling of LLM training compute appears to be hitting a plateau, with more incremental gains in its latest model, Orion, than hoped relative to GPT-4. Reports of this slowing trend are not isolated to OpenAI. Google DeepMind, for instance, is expected to launch Gemini 2.0 in December, but reports have also suggested internal disappointment on improvements. Similarly, we recently discussed Anthropicβs delayed release of Opus 3.5, though CEO Dario Amodei has now confirmed they still plan to release it. Meanwhile, Metaβs LLaMA 4 and XAiβs Grok-3 are currently training on clusters of more than 100,000 H100 GPUs, with Grok-3 expected as soon as late 2024. Despite these investments, the anticipated performance gains across models may be smaller than the leap seen with previous generations, raising broader questions about the limits of traditional training compute βscaling laws.β
In May, OpenAI CEO Sam Altman expressed high hopes for Orion, the companyβs upcoming flagship model, predicting it would be significantly more advanced than GPT-4. At the time, Orionβs training was reportedly only 20% complete, but it was already performing on par with GPT-4 in tasks and intelligence. However, as training progressed, Orionβs improvements have been more incremental, especially compared to the leap seen between GPT-3 and GPT-4, leading some within OpenAI to temper their expectations. As testing continues, OpenAI employees who have worked with Orion report that, while it shows notable progress on certain language tasks, its performance is inconsistent, particularly with more structured tasks like coding and complex problem-solving. For some applications, Orionβs capabilities donβt clearly surpass GPT-4. These mixed results have raised questions about whether Orionβs enhancements are enough to justify its increased operational costs.
OpenAI has yet to finish the final safety evaluations of Orion, which are expected to be publicly released early next year, with hints that it may depart from the traditional βGPTβ branding to reflect its new direction. It is also possible that Orion is integrated into OpenAIβs new o1 reasoning model family to achieve further performance gains.
Why should you care?
Exactly what bottlenecks or dead ends will get in the way of continuing to improve LLM capabilities is a key factor in how quickly they will significantly transform the global economy and potentially even achieve AGI. While diminishing returns are natural to some extent β particularly after saturating many easier capabilities and benchmark tasks β LLMs still have a long way to go to match human performance on many tasks. We actually think current LLM capabilities are already enough for a huge global impact, but foundation LLMs need to be customized to specific tasks and companies to achieve the reliability and productivity gains needed for widescale adoption. This is currently bottlenecked by LLM Developer talent (we think many millions of LLM Developers will be needed, and we are trying to solve this with our Towards AI Academy), employeeβs non-technical LLM education, and the time it takes to test and iterate these advanced LLM pipelines. Nevertheless, progress in foundation model capabilities can open up new use cases, and we would be very disappointed if progress stops here! However, we donβt think this is likely.
Despite recent press reports on disappointing gains from larger training compute budgets β Sam Altman and Dario Amoedi are both very optimistic in public statements (albeit with an incentive given fundraising needs!). Sam Altman, for example, said in a recent Reddit AMA he thinks AGI can be βachievable with current hardware.β Dario Amoedi meanwhile thinks βPowerful AIβ will be achieved in 2026 or 2027. Recent newsflow from leading cloud providers lining up nuclear power for the energy needs of next-generation training clusters also contradicts the saturating returns narrative. Nevertheless, we think there have likely been disappointing results from training runs this year as companies have scaled to 50k+ H100 GPU clusters. Most likely, this is due to a bottleneck in diverse training data after saturating data that is easily scrapable from the internet. New data (real or synthetic) and new architectures may be needed to make the most of larger clusters. Training compute is not the only path to progress. However, we think huge progress has been made this year in both inference cost, and the underappreciated new inference compute scaling paradigm.
Foundation LLM capability improvement comes broadly from six sources: 1) Increased training compute budget (which is from more GPUs/TPUs, better GPUs, or longer training runs and can be spent on more parameters, more training data, or more FLOPs per forward/backward pass). 2) Increased utilization of this training compute (higher Maximum FLOPS Utilization, less downtime), 3) Higher quality training data, 4) More training compute efficient algorithms (e.g., MoE, new attention mechanisms), 5) Better mid-training/post-training performance unlocks and enhancement (e.g., RLHF, instruction tuning, monte carlo tree search) and 6) more recently Inference or Test Time compute scaling (increasing βthinking time/tokensβ to solve harder problems). We think right now inference compute scaling is the most effective path to progress, so we would not be surprised to see the focus shift here from scaling training compute. We also think there is still a lot of room to experiment with new or modified model architectures. However, we think larger and larger training budgets will still be justified in parallel, given that even incremental gains relative to other techniques can still unlock huge economic value.
β Louie Peters β Towards AI Co-founder and CEO
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Repositories & Tools
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Quick Links
1. Scale AI announced Defense Llama. Built on Metaβs Llama 3, the LLM is specifically customized and fine-tuned to support American national security missions. Defense Llama is available exclusively in controlled U.S. government environments within Scale Donovan. It aims to apply the power of generative AI to use cases such as planning military or intelligence operations and understanding adversary vulnerabilities.
2. Microsoft researchers recently unveiled a new multi-agent infrastructure called Magentic-One that allows a single AI model to power various helper agents that work together to complete complex, multi-step tasks in different scenarios. Microsoft calls Magentic-One a generalist agentic system that can βfully realize the long-held vision of agentic systems that can enhance our productivity and transform our lives.β
3. The Beatlesβ AI-assisted track βNow and Thenβ is nominated for two Grammy awards. Though the band has been broken up for 50 years, Paul McCartney used AI last year to create βthe last Beatles record.β He took one of Lennonβs demos from 1978 and used AI to clean up the recordingβs poor sound quality.
4. Another one of OpenAIβs lead safety researchers, Lilian Weng, announced she is departing the startup. Weng served as VP of research and safety since August and, before that, was the head of the OpenAIβs safety systems team. In a post on X, Weng said, βAfter 7 years at OpenAI, I feel ready to reset and explore something new.β
5. OpenAI defeats news outletsβ copyright lawsuit over AI training. A New York federal judge dismissed a lawsuit against artificial intelligence giant OpenAI that claimed it misused articles from news outlets Raw Story and AlterNet to train its large language models.
6. OpenAIβs o1 Model Leaked on Friday, and It Is Wild β Hereβs What Happened
OpenAIβs upcoming AI model, o1, was accidentally leaked, showcasing advanced capabilities surpassing GPT-4, including comprehensive image and multimedia analysis. The leak occurred due to a URL parameter modification, but OpenAI has since resolved the issue, with an official release anticipated soon.
Whoβs Hiring in AI
PhD Intern (f/m/d) β Business AI Research @SAP (Berlin, Germany)
Research Engineer @Anthropic (London, UK)
Staff Software Engineer, Generative AI, Gemini Code Assist @Google (New York, NY, USA)
Applied Machine Learning Engineer β Localization @Apple (Cupertino, California, United States)
Generative AI Engineer @FabFitFun (USA/Remote)
AI Engineer @SmartDev (Hanoi, Vietnam)
AI & GenAI Data Scientist-Senior Associate @PwC (Multiple Locations)
Interested in sharing a job opportunity here? Contact [email protected].
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