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As artificial intelligence continues to advance, not all capabilities progress at the same pace. That’s the core insight behind the concept of the “reinforcement gap” a dividing line between AI skills that benefit strongly from reinforcement learning and those that do not. In a recent TechCrunch article, Russell Brandom argues that as AI development leans heavily on reinforcement learning (RL), tasks that are objectively measurable are accelerating ahead, while subjective ones lag behind.
What Is the Reinforcement Gap?
In essence, the reinforcement gap refers to the growing disparity between AI skills that can be improved via clear pass/fail feedback loops (ideal for RL) and skills that are much harder to quantify. Brandom highlights coding tasks like bug fixes, algorithm optimization, or test-based criteria as ideal candidates for RL because they come with rich, repeatable feedback. Meanwhile, tasks like crafting prose, designing interfaces, or subtle conversation remain harder to train using RL methods.
Because reinforcement learning thrives when the system can try, fail, adjust, and retry at scale, AI models focusing on “testable” domains improve rapidly. In contrast, domains without obvious test metrics such as translation quality, storytelling, or highly contextual advice tend to evolve more slowly.
Why Coding and Math Advance Quickly
Software development is uniquely suited to reinforcement learning for several reasons:
- Existing test suites: Unit tests, integration tests, performance benchmarks all standard in development can validate code objectively.
- Automated evaluation: Models can be scored automatically, enabling millions of training iterations without human intervention.
- Clear objectives: Success criteria (like passing tests or improving performance) are well-defined and measurable.
As Brandom notes, these features allow AI tools to iterate faster, catch regressions, and self-correct pushing progress at an accelerating rate in domains like code generation and optimization.
Why Subjective Tasks Lag Behind
In contrast, tasks like writing an email, generating persuasive copy, or engaging in nuanced conversation are harder to evaluate objectively. What makes one email “better” than another often depends on tone, context, or personal preference. Because these tasks lack clear, scalable feedback loops, reinforcement learning offers limited leverage.
Even when the underlying large language model improves, products built on top like chatbots or writing assistants might not noticeably benefit if they juggle multiple tasks or lack domain-specific performance signals.
Surprising Exceptions & Emerging Frontiers
Not every task is strictly “testable” or “non-testable.” Brandom points out that in some domains (like video generation), what seems subjective can become measurable. The example is OpenAI’s Sora 2 model: faces keep shape, objects don’t blink in and out, and physics consistency improves. These qualities can potentially be translated into quantifiable criteria, making them RL-amenable.
This suggests that the reinforcement gap is not fixed. As AI tooling becomes more sophisticated in measuring human-centric quality, it may shrink or shift. But for now, Brandom argues, the gap is widening and determining which skills get automated first.
Implications for Startups, Automation & Jobs
The reinforcement gap has profound implications:
- Automation will concentrate: Jobs rooted in testable tasks (e.g. software testing, algorithmic roles) may face faster automation.
- Product design matters: Startups may succeed by identifying parts of workflows that are RL-friendly and automating them first.
- Job shifts ahead: Skills heavily dependent on nuance, creativity, or context may retain stronger human involvement longer.
- Economic restructuring: Entire domains may transform if core tasks become easily evaluable by machines.
As Brandom puts it: if a process ends up on the “right side” of the reinforcement gap, it’s likely to be automated. And anyone currently doing such work might see new pressures to adapt or reskill.
The reinforcement gap invites us to rethink how we build AI systems. Instead of assuming uniform progress across tasks, the gap suggests progress will be uneven favoring domains where success can be measured and repeated. This is not a permanent barrier, but a current artifact of how RL is being used. As evaluation methods evolve, we may see the gap shift. In the meantime, identifying where AI advances fastest can guide smarter strategies for product development, job design, and tech policy.
For deeper analysis of how AI reinforcement learning shapes real-world tools and industries, check out my post on reinforcement learning’s impact across sectors.