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Can AIs Train Themselves Better Than We Can?
🔥 What if the best teachers for AI… are the AIs themselves?
In this episode, we dive deep into a groundbreaking new approach to training large language models (LLMs) that could completely redefine how AI learns. No human labels. No feedback loops. Just internal logic and the model’s own understanding.
📌 Here’s what you’ll learn:
Why the traditional “humans teach AI” setup is becoming a bottleneck as models begin outperforming us on some tasks;
How the algorithm Internal Coherence Maximization (ICM) allows models to generate and learn from their own training labels;
Why this approach works better than crowdsourced labels—and in some cases, even better than “perfect” golden labels;
How ICM activates latent knowledge already present in the model, without external instruction;
How this method scales all the way up to production-level systems, including training assistant-style chatbots without any human preference data.
🤯 Key insights:
In some tasks, models trained without humans performed better than those trained with human feedback;
ICM can surface and enhance abilities that humans can’t reliably describe or evaluate;
This opens the door to autonomous self-training for models already beyond human-level at certain tasks.
đź’ˇ Why this matters:
How do we guide or supervise AI when it’s better than us? This episode isn’t just about algorithms—it’s about a shift in mindset: from external control to trusting the model’s internal reasoning. We’re entering a new era—where AIs not only learn—but teach themselves.
🎧 Subscribe if you’re curious about:
The future of artificial intelligence;
Training models without human intervention;
New directions in AI alignment;
And where this path might ultimately lead.
👉 Now a question for you, the listener:
If models can train themselves without us, does that mean we lose control? Or is this our best shot at building safer, more aligned systems? Let us know in the comments!
Key takeaways:
ICM fine-tunes models without external labels—using internal logic alone.
The approach outperforms human feedback on certain benchmarks.
It scales to real-world tasks, including chatbot alignment.
Opens a new frontier for developing superhuman AI systems.
SEO tags:
Niche: #LLMtraining, #AIalignment, #ICMalgorithm, #selfsupervisedAI
Popular: #artificialintelligence, #chatbots, #futureofAI, #machinelearning, #OpenAI
Long-tail: #modelselftraining, #unsupervisedAIlearning, #label-freeAItraining
Trending: #AI2025, #postGPTera, #nohumanfeedback
Read more: https://alignment-science-blog.pages.dev/2025/unsupervised-elicitation/paper.pdf
View all episodes
By j15🔥 What if the best teachers for AI… are the AIs themselves?
In this episode, we dive deep into a groundbreaking new approach to training large language models (LLMs) that could completely redefine how AI learns. No human labels. No feedback loops. Just internal logic and the model’s own understanding.
📌 Here’s what you’ll learn:
Why the traditional “humans teach AI” setup is becoming a bottleneck as models begin outperforming us on some tasks;
How the algorithm Internal Coherence Maximization (ICM) allows models to generate and learn from their own training labels;
Why this approach works better than crowdsourced labels—and in some cases, even better than “perfect” golden labels;
How ICM activates latent knowledge already present in the model, without external instruction;
How this method scales all the way up to production-level systems, including training assistant-style chatbots without any human preference data.
🤯 Key insights:
In some tasks, models trained without humans performed better than those trained with human feedback;
ICM can surface and enhance abilities that humans can’t reliably describe or evaluate;
This opens the door to autonomous self-training for models already beyond human-level at certain tasks.
đź’ˇ Why this matters:
How do we guide or supervise AI when it’s better than us? This episode isn’t just about algorithms—it’s about a shift in mindset: from external control to trusting the model’s internal reasoning. We’re entering a new era—where AIs not only learn—but teach themselves.
🎧 Subscribe if you’re curious about:
The future of artificial intelligence;
Training models without human intervention;
New directions in AI alignment;
And where this path might ultimately lead.
👉 Now a question for you, the listener:
If models can train themselves without us, does that mean we lose control? Or is this our best shot at building safer, more aligned systems? Let us know in the comments!
Key takeaways:
ICM fine-tunes models without external labels—using internal logic alone.
The approach outperforms human feedback on certain benchmarks.
It scales to real-world tasks, including chatbot alignment.
Opens a new frontier for developing superhuman AI systems.
SEO tags:
Niche: #LLMtraining, #AIalignment, #ICMalgorithm, #selfsupervisedAI
Popular: #artificialintelligence, #chatbots, #futureofAI, #machinelearning, #OpenAI
Long-tail: #modelselftraining, #unsupervisedAIlearning, #label-freeAItraining
Trending: #AI2025, #postGPTera, #nohumanfeedback
Read more: https://alignment-science-blog.pages.dev/2025/unsupervised-elicitation/paper.pdf