Making the case to policy-makers re: $500B Theatrical Investment in AI Supremacy...
If we want to convey to the White House (or any policymaker) that $500B over 4 years might not buy us a “GPT-5 that revolutionizes AI,” we should emphasize:
Diminishing returns from scale alone
- The “low-hanging fruit” of web-scale text has mostly been used. Going bigger just to vacuum up low-quality or synthetic data yields limited improvement.
- There’s an inflection point at which more data and parameters do not translate into proportionally better results.
Model collapse risk
T- raining on synthetic data can introduce feedback loops that degrade model quality over time, unless a lot of extra work is done.
- Solving this is not just a matter of more GPUs but of fundamentally better data curation and model design.
Need for new research directions
True leaps might come from architectural innovations, grounded or multimodal learning, and Continuous RLHF...
What is Continuous RLHF?
1. Understanding RLHF (Reinforcement Learning from Human Feedback)
What is RLHF?
RLHF is a technique for refining a model’s behavior using feedback from human evaluators.
Instead of a purely “unsupervised” or “self-supervised” approach (like massive next-word prediction on text corpora), RLHF tries to align model outputs with what humans consider “high-quality,” “helpful,” or “safe.”
Basic Workflow
Train a baseline model: Often with self-supervised learning on large text corpora.
Collect human preference data: Show model outputs (or pairs of outputs) to real people, who choose which output is better.
Train a reward model: This learns to predict the preference judgments from Step 2.
Fine-tune the baseline model with reinforcement learning (RL), maximizing the learned “reward model” score.
Why RLHF?
It helps incorporate human values and preferences directly, hopefully guiding the model to produce more “helpful” or “non-toxic” responses.
Major large-scale models like ChatGPT, GPT-4, and Bard use RLHF to refine their outputs after initial pre-training.
2. What Does “Continuous RLHF” Mean?
The term “Continuous RLHF” isn’t a standardized industry phrase, but it commonly implies one or more of the following:
New, Unseen Human Feedback:
You’re continually collecting human preference judgments on new or recently generated prompts and model outputs, rather than reusing an old dataset from months or years ago.
Freshness here means the feedback isn’t stale or based solely on previously seen queries or previously generated model outputs.
Live or Iterative Feedback Loops:
Instead of a one-off RLHF training round (like “fine-tune the model once and done”), the model is put into some environment or scenario where it interacts with human users in real time.
Those interactions generate a continuous stream of new (fresh) feedback signals. The model can then be updated (in small or large increments) so it doesn’t stagnate.
Avoiding Self-Collapse Through Recycled Data:
“Fresh RLHF” can be about preventing “model collapse,” where models get trained on their own synthetic output repeatedly, reintroducing or even amplifying their own errors/biases.
By continually incorporating human-labeled or human-vetted data from novel contexts, you limit the risk of drifting into an echo chamber of your model’s own mistakes.
Why It Matters
If you only train RLHF once, or if you rely on feedback on the same set of prompts and outputs, the model’s improvement plateaus. The process no longer helps it handle new topics or edge cases.
If you keep reusing the model’s own generated text (possibly containing errors or biases) as training data without fresh, external human corrections, you can amplify flaws over time. That’s the “model collapse” phenomenon.
Having fresh, diverse, human feedback is vital to keep the reward model honest, relevant, and robust to new challenges.
3. Why “Freshness” is Hard (Especially at Scale)
Cost and Logistics
Gathering new, high-quality human feedback is expensive. You need large-scale annotation pipelines, well-paid raters, and platforms that handle data at the scale of tens or hundreds of thousands of prompts.
It’s not just the cost in dollars for labeling—it’s also the overhead of managing data pipelines, ensuring consistency and quality among raters, etc.
Diminishing Returns
If the model is extremely capable, the cost of generating truly challenging or novel tasks (where human insight is still needed) goes up.
It’s easy to get feedback on “mundane” prompts, but that feedback might not help the model advance in more complex or specialized domains.
Risk of Overfitting Even with RLHF
If the prompts or tasks used in RLHF are not broad or varied enough, the model may optimize for a narrow set of “preference metrics” rather than actually gaining general intelligence.
Freshness doesn’t just mean “new samples.” It also means ensuring coverage of the long tail of topics, complexities, and contexts you want the model to handle.
4. Tying It Back to Your Larger Pursuit: Avoiding Model Collapse & Stagnation
You’re trying to convince policymakers (like those in the White House) that a huge investment (e.g., $500B) spent purely on bigger data centers and GPU farms may not suffice to create a substantially more advanced GPT-5 if:
All the Real Data is “Used Up”
GPT-4 and other large models have already trained on most of the easy-to-get, high-quality human-generated text.
Additional “raw internet data” either is too low-quality or too redundant to yield major breakthroughs in the model’s capability.
Synthetic Data Alone Risks Collapse
Simply having GPT-4 generate more text to train GPT-5 can create feedback loops (the “model collapse” problem).
If you do nothing but feed the model’s outputs back into itself, you risk a downward spiral of errors or unaddressed biases accumulating.
Fresh RLHF is Expensive & Non-Trivial
Continually collecting high-quality human feedback on new prompts, tasks, or domains is not just a matter of spinning up more GPU clusters.
It requires a robust human feedback pipeline—possibly tens of thousands (or more) of trained annotators, domain experts, or real-time user interactions.
You also need to address legal, ethical, and privacy aspects of collecting large-scale human feedback.
Thus, “Continuous RLHF” stands out as one potential—but resource-heavy—way to keep models from stagnating and collapsing when you can’t rely on infinite new real-world text. It helps ensure the model remains grounded in actual human judgments, rather than echoing its own generative quirks.
5. Key Takeaways
“Continuous RLHF” is about continual, new, and high-quality human feedback loops, rather than reusing old or purely synthetic data.
It mitigates model collapse by injecting genuine external references and corrections—keeping the model from amplifying its own errors.
It’s crucial for pushing beyond the data frontiers that GPT-4 has arguably saturated.
It’s not cheap or trivial; it requires significant infrastructure, people, workflows, and possibly new research on how to make RLHF scalable and robust.
Therefore, a huge investment in hardware alone does not guarantee model progress if we neglect ongoing, high-quality, fresh human (or environment) feedback.
Bottom Line
The phrase “Continuous RLHF” can be summarized as regularly collecting and incorporating brand-new human feedback to keep model training signals from becoming stale or self-reinforcing.
It’s a partial solution to model collapse and the broader challenge of data saturation.
Implementing it at scale is far from trivial and far from free—so any large-scale AI funding strategy must allocate resources (and intellectual capital) towards these human-in-the-loop processes, not just raw compute.