LLMs Have Hit the Wall
AIM Research
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OpenAI’s latest endeavours are stirring up the AI community. While some experts argue that LLMs have reached their peak, OpenAI chief Sam Altman disagrees vehemently. “There is no wall,” Altman said , challenging the notion that scaling LLMs is hitting a dead end.
Scaling the Right Things Matter?
Former OpenAI co-founder Ilya Sutskever, now leading Safe Superintelligence (SSI), echoed Altman’s optimism. “Scaling the right thing matters more now than ever,” said Sutskever , who is also working on similar lines as OpenAI but with a different approach. He’s most likely focusing on reinforcement learning and self-supervised methods to build safe superintelligence.?
OpenAI is implementing a strategy where each new model, like GPT-5 or GPT-6, is trained on high-quality synthetic data generated by its predecessor, Strawberry (o1 ). This creates a “recursive improvement cycle”, enhancing model performance incrementally with each iteration.
Andrej Karpathy , former OpenAI co-founder and founder of Eureka Labs, highlighted a critical gap: LLMs lack thought process data. Karpathy introduced the concept of “jagged intelligence ”, stressing the need for models to develop cognitive self-knowledge through sophisticated post-training methods rather than merely mimicking human labels.
‘I Told You So’
Meta’s chief AI scientist, Yann LeCun , didn’t hold back his scepticism about OpenAI’s approach. “I don’t want to say ‘I told you so’, but I told you so!” LeCun remarked, highlighting Meta’s parallel efforts in developing autonomous machine intelligence (AMI).
Under LeCun’s leadership, Meta has merged FAIR and the GenAI team to create AMI, a system designed to emulate human and animal reasoning. This year, Meta released V-JEPA (Video Joint Embedding Predictive Architecture), enhancing machine understanding through video interactions and several advanced models like SAM 2.1 and Meta Lingua , which optimise LLM performance without specialised hardware.
Meta’s Layer Skip selectively executes layers in LLMs, accelerating generation times. Also, the upcoming Llama 4 leverages self-supervised learning to enable broad data representation and flexibility across domains. Meta’s new ‘self-taught evaluator ’ employs chain-of-thought techniques to improve model accuracy in complex fields like science and mathematics.
LeCun remains steadfast in his belief that new architectures and paradigms are essential for achieving human-level AI. “Reaching human-level AI will require new architectures and new paradigms,” he replied to Gary Marcus , underscoring Meta’s commitment to advancing beyond current LLM limitations.
Enjoy the full story here .?
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