AI: OpenAI AI coding push a broader AGI harbinger. RTZ #634

...AI reinforcement learning (RL) reinvented and accelerated

I’ve recently discussed OpenAI’s accelerated focus on AI software coding and engineering for the tens of millions of developers globally. A recent AI paper by the company provides more technical details on the company’s approach that has wider implications for the AI Tech Wave this year.

The leading LLM AI companies are accelerating their efforts on AI Reasoning and Agentic capabilities, especially after DeepSeek’s open source rollouts of LLM AI v3 and AI reasoning product R1. OpenAI as I recounted in recent posts has simplified its upcoming product roadmap on its way to AGI, while planning to fuse LLM AI and AI Reasoning/Agents into GPT-5 later this year. That after releasing GPT 4.5 in the coming weeks.

I highlight all this because the company is also in the process of releasing its ‘software engineer’ augmentation program, which showcases the new approaches to accelerate AI reasoning and Agents being implemented by peers like DeepSeek, Google, Anthropic, Perplexity, and others.

As AI entrepreneur Matthew Berman explains in a recent tweet/x thread:

“OpenAI just dropped a paper that reveals the blueprint for creating the best AI coder in the world. But here’s the kicker: this strategy isn’t just for coding—it’s the clearest path to AGI and beyond.”

“1/ OpenAI’s latest research shows that reinforcement learning + test-time compute is the key to building superintelligent AI. Sam Altman himself said OpenAI’s model went from ranking 175th to 50th in competitive coding—and expects #1 by year-end.”

“2/ The paper, “Competitive Programming with Large Reasoning Models,” compares different AI coding strategies. At first, models relied on human-engineered inference strategies—but the biggest leap came when humans were removed from the loop entirely.”

“3/ Enter DeepSeek-R1, a model that cost only ~$5M to train. Its breakthrough? Reinforcement learning with verifiable rewards. This method, also used in AlphaGo, let's the model learn from trial & error, and scale intelligence indefinitely.”

“3/ Enter DeepSeek-R1, a model that cost only ~$5M to train. Its breakthrough? Reinforcement learning with verifiable rewards. This method, also used in AlphaGo, let's the model learn from trial & error, and scale intelligence indefinitely.”

“4/ Think about it this way: AlphaGo became the best Go player in the world without human guidance. It just kept playing itself until it mastered the game. Now, OpenAI is applying the same principle to coding—and soon, to all STEM fields.”

“5/ What does this mean? Every domain with verifiable rewards (math, coding, science) can be mastered by AI just by letting it play against itself. AI is removing human limitations—and that’s how we get to AGI.”

“6/ Here’s the data from the coding competition:

• GPT-4: 808 ELO (decent)

• OpenAI-01: 1,673 ELO (better)

• OpenAI-03: 2,724 ELO (SUPERHUMAN)

“99.8th percentile of competitive coders, with no human-crafted strategies.”

This approach has worked in other AI domains as well:

“7/ Tesla did this with Full Self-Driving. They used to rely on a hybrid model (human rules + AI). But when they switched to end-to-end AI, performance skyrocketed. AI just needs more compute—not more human intervention.”

“8/ The takeaway? Sam Altman was right when he said AGI is just a matter of scaling up. Reinforcement learning + test-time compute is the formula for intelligence—and OpenAI is already proving it.”

“9/ We’re witnessing the birth of AI superintelligence in real time. It won’t stop at coding. The same techniques will make AI the best mathematician, scientist, and engineer in history. The race to AGI is on.”

“Here's the paper: https://arxiv.org/pdf/2502.06807 :”

The youtube.com rundown by Berman is worth a watch for some video illustrations of the OpenAI technology.

The bigger takeaway is that AI Reasoning is getting a ‘Reinforcement Learning’ redo with unsupervised self-learning approaches. It’s AI reinforcement learning (RL) without supervision, now accelerating 'test-time compute' distillation driven inference & training.

And that approach is likely to show rapidly scaling results in a wide range of fields beyond software coding and engineering.

It’s all just getting started. Stay tuned.

(NOTE: The discussions here are for information purposes only, and not meant as investment advice at any time. Thanks for joining us here)