Co-Designing AI Products and Models for Specialized RL Application

Latent Space: The AI Engineer Podcast · December 30, 2025 · Listen to Original Episode →

Original Title:

TL;DR

RL's application to LLMs currently exhibits "peaky" generalization, excelling within training distributions but failing to broadly transfer, necessitating the integration of economically valuable tasks into the model's training context.
The "death of one model fits all" signifies a shift towards specialized models, driven by organizational structures and data limitations, rather than inherent model capacity constraints.
Academic research often over-rewards complex mathematical ideas over simple, generalizable solutions, leading to overfitting benchmarks rather than developing robust, broadly applicable AI.
OpenAI's internal research progression is characterized by steady, incremental improvements rather than dramatic public leaps, with external releases lagging significantly behind internal capabilities.
The convergence of multiple labs on similar RL approaches suggests a shared understanding of effective techniques, leading to a renewed frontier where distinct approaches yield comparable results.
Co-designing products and models is crucial for AI advancement, enabling context integration and faster feedback loops, particularly in domains like coding where context is more contained.
The transition from robotics to LLMs is facilitated by shared needs for extensive data analysis and problem-solving, fostering a cross-disciplinary talent pool in AI.

Deep Dive

The core argument is that Reinforcement Learning (RL) has emerged as a critical tool for advancing AI capabilities, particularly in complex reasoning tasks, but its effectiveness is heavily dependent on how it's applied and integrated with product development. This realization is driving a shift towards co-designing models and products, especially in specialized domains like coding, to overcome the limitations of general-purpose RL and achieve more practical, economically valuable AI.

The transition from large-scale, general AI to domain-specific applications is a key implication. While scaling laws have driven significant progress, the speaker suggests that RL's current application in LLMs is "peaky," meaning it excels in specific areas but doesn't generalize broadly to all tasks or industries. This necessitates bringing the "distribution of economically useful tasks" into the model's training environment, rather than expecting the AI to magically adapt. For example, in coding, the codebase and terminal interactions provide a contained but rich context for RL to operate effectively. This focus on context and product integration is what makes companies like Cursor uniquely positioned; they can co-design their product and models, allowing for rapid iteration and alignment between user needs and AI capabilities, such as the "two-hour policy updates" mentioned for their Composer tool.

A secondary implication is the re-evaluation of the "one model fits all" paradigm. The speaker points to OpenAI's internal shifts and public statements as evidence that specialized models or approaches within larger frameworks are becoming more relevant. This is not necessarily a failure of model capacity but an organizational reality where different teams focus on specific domains, leading to specialized AI capabilities. Furthermore, the discussion highlights a potential misconception about the nature of AI progress. While media often portrays AI development as a series of dramatic leaps, the reality within research labs is often a more gradual, iterative process of experimentation and scaling. This steady, incremental improvement, driven by internal tooling and a deep understanding of data, is crucial for sustained progress, especially as the industry grapples with the challenges of continual learning and data efficiency.

Action Items

Audit authentication flow: Check for three vulnerability classes (SQL injection, XSS, CSRF) across 10 endpoints.
Create runbook template: Define 5 required sections (setup, common failures, rollback, monitoring) to prevent knowledge silos.
Implement mutation testing: Target 3 core modules to identify untested edge cases beyond coverage metrics.
Profile build pipeline: Identify 5 slowest steps and establish 10-minute CI target to maintain fast feedback.

Key Quotes

"we probably kind of overfit to the benchmarks pretty heavily and you know how i see this in retrospect is that we gave ourselves a lot of like new knobs to tune and then implicitly kind of tuned those to fit the benchmarks everyone knew that we're doing that at some level but i think it's hard to appreciate like that it's not just happening for a single paper at kind of like a meta level for the whole community that's happening too"

The speaker reflects on the tendency in AI research to optimize heavily for benchmarks, suggesting this can lead to overfitting where models perform well on specific tests but may not generalize effectively to real-world applications. This practice, occurring at both individual paper and community levels, can result in research that is less broadly useful.

"I think my view on it now is that it feels like LLM agents are going to be like a trillion dollar market before robotics is maybe even like a 10 billion market and this is just because so I mean you know LLM agents already create value out in the world whereas robotics it's like kind of hard to make the case that like you know kind of AI robotics like does anything that usefully yet"

This statement highlights a perceived difference in the immediate value proposition of LLM agents compared to AI-powered robotics. The speaker suggests that LLM agents are already demonstrating practical utility and market potential, while advanced robotics still faces challenges in proving its usefulness and economic viability.

"I think my view is something like RL the way it's applied to LLMs right now was kind of a weird funny tool where it doesn't really generalize beyond the training distribution that much... it's very peaky right like it it kind of it can kill the training distribution like completely... but yeah it doesn't really generalize"

The speaker expresses a nuanced view on the application of Reinforcement Learning (RL) to Large Language Models (LLMs). While acknowledging RL's potential, they emphasize its current limitation of poor generalization beyond the specific data it was trained on, describing its performance as "peaky" and potentially detrimental to the training distribution.

"The idea was you you started with codex someone else was doing instruction gpt then we launched gpt four four I guess O1 and O1 was kind of a supposed to be like a reasoning one model fits all and there we merged the four O and O1 O3 line into five and now we're splitting it out into five and five codex again it's like it's just a weird well OpenAI has a tendency to ship the org chart basically"

This quote critiques the organizational strategy behind OpenAI's model development, suggesting that product releases and architectural decisions have sometimes reflected internal restructuring rather than a consistent technological vision. The speaker points to the evolution of models like Codex and GPT-4 as examples of this trend, implying a lack of a singular "one model fits all" approach.

"I think people have basically figured out in one way or another to like achieve more or less the same thing [with RL]... the song about the move to cursor yeah why is cursor accumulating and drawing so many cool RL people"

The speaker observes a convergence in the field of Reinforcement Learning (RL) among major AI labs, suggesting that multiple organizations have arrived at similar effective techniques. This observation leads into a question about why Cursor, a company, is attracting significant talent in the RL space, implying a specific draw or opportunity there.

"I think the ML group is great... it's like you know it's just like 20 25 people and you know I was like honestly like pleasantly like very very surprised that like how good composer is like given the size of the group and you know it's not like a big research lab"

This quote highlights the speaker's positive assessment of the machine learning team at Cursor. Despite its relatively small size, the team has developed a highly effective product, "Composer," which has impressed the speaker and suggests a high level of talent and efficiency within the group.

Resources

External Resources

Books

"Science of Deep Learning" - Mentioned in relation to understanding how different layers are initialized for good scale.

Articles & Papers

"Online Tab" (Jacob Jackson's blog post) - Discussed as an example of rapid policy updates (every two hours) enabled by a smaller, focused organization.

People

Lex Friedman - Mentioned as having dinner with the speaker and sharing an assessment of robotics people.
Ilya Sutskever - Mentioned as one of the people responsible for OpenAI's conviction and pursuit of AGI.
Jacob Pachachi - Mentioned as one of the people responsible for OpenAI's conviction and pursuit of AGI.
Sam Altman - Mentioned in relation to being fired from OpenAI.
Mark Chen - Mentioned as a senior OpenAI person who stated that OpenAI is no longer doing "one model fits all."
Leo Pinto - Mentioned as a former intern from OpenAI's 2017 robotics team, now at NYU.
Eric - Mentioned as someone who led reasoning at XAI and worked on K-FAC.
Jacob Jackson - Mentioned for his blog post about "Online Tab."

Organizations & Institutions

Nurob - Mentioned as the location where the special coverage was recorded.
Cursor - Mentioned as the company the speaker joined and its focus on co-designing products with models.
OpenAI - Mentioned as the speaker's former employer and a significant player in AI research.
Berkeley - Mentioned as the speaker's alma mater for their PhD.
XAI - Mentioned in relation to a former employee working on K-FAC.
Physical Intelligence - Mentioned as a company with impressive robotics demonstrations.
Sunday - Mentioned as a company with robotics demonstrations.
Microsoft - Mentioned in the context of OpenAI's governance and potential acquisition.
DeepSeek - Mentioned in relation to a moment that changed perceptions about NVIDIA chips.
Anthropic - Mentioned in relation to their models (Opus 2 4 5) showing similar RL plots.

Websites & Online Resources

Pro Football Focus (PFF) - Mentioned as a data source for player grading in a previous context.

Other Resources

AGI (Artificial General Intelligence) - Mentioned as a long-term goal and a topic of discussion regarding governance.
RL (Reinforcement Learning) - Discussed as a key methodology for achieving AGI and its application in LLMs.
K-FAC - Mentioned in relation to work done by a former OpenAI employee.
GPT-4 - Mentioned as a reasoning model from OpenAI.
O1 - Mentioned as a reasoning model from OpenAI.
O3 - Mentioned as a reasoning model from OpenAI.
O1003 - Mentioned as a reasoning model from OpenAI.
GPT-5 - Mentioned as a model from OpenAI.
O1, O3, O5, O5 Codex - Mentioned as a series of models from OpenAI.
GDP Val - Mentioned as a benchmark for evaluating models on economically useful tasks.
RL Agents - Discussed as a potentially trillion-dollar market.
LLM Agents - Discussed as a potentially trillion-dollar market.
K-FAC - Mentioned in relation to work done by a former OpenAI employee.
O1, O3, O5, O5 Codex - Mentioned as a series of models from OpenAI.
GDP Val - Mentioned as a benchmark for evaluating models on economically useful tasks.
RL Agents - Discussed as a potentially trillion-dollar market.
LLM Agents - Discussed as a potentially trillion-dollar market.
Composer - Mentioned as the main focus of Cursor's ML group and a product that is smart and fast.
Data Dog - Mentioned as a tool for looking at what's happening in software engineering.
Blip - Mentioned as a significant event at OpenAI involving Sam Altman's firing.
Scaling Laws - Discussed in relation to whether they are dead.
Human Feedback - Discussed as a method for model improvement that is limited by compute.
Tool Use - Mentioned as a feature added to models.
I/O I - Mentioned in relation to scaling laws.
I/O M - Mentioned in relation to scaling laws.
IMR - Mentioned as a benchmark that OpenAI's models could surpass.
IOI - Mentioned as a benchmark that OpenAI's models could surpass.
Math Exam - Mentioned in relation to predictions made at a conference.
Humanities Exam - Mentioned in relation to predictions made at a conference.
Dyson Spheres - Mentioned in relation to long-term predictions made at a conference.
EA (Effective Altruism) - Mentioned in relation to a community that makes predictions.
NVIDIA chips - Mentioned in relation to DeepSeek's impact.
R10 - Mentioned as a cool branch from DeepSeek.
RKGI2 plot - Mentioned as a plot seen in Anthropic models.
Continual Learning - Discussed as a significant theme and a potential area for breakthroughs.
Information Theory - Mentioned as a topic for a podcast.
CPU view of neural networks - Mentioned as a perspective on how neural networks function.
Hard drive view of neural networks - Mentioned as a perspective on how neural networks function.