Sleep plays an important role in maintaining neuronal circuitry, signalling and helps maintain overall health and wellbeing. Sleep deprivation (SD) disturbs the circadian physiology and exerts a negative impact on brain and behavioural functions.
Considering loops of “thinking out loud” and the “verify one step at a time” research has been helpful in improving model performance, would it make sense to have sleep inspired research?
If I was given a job at OpenAI’s research team and given the liberty to explore any task, this would be the first thing I’d work on.
After all, even a genius on 300h without sleep would start to hallucinate.
AI models use pretrained weights. Resting or loaded to 100% for a year, they output the same data. There is no connection between API calls.
One has to rethink your definition and intention to get an answer that is practical and keeps this topic from going in the rubbish bin. One also has to push an AI out of the box to get it to write.
Power management, or “sleep,” is crucial for data centers running GPU-based LLM inference servers. Allowing GPUs to enter low-power states during idle periods drastically reduces energy consumption, cutting electricity costs and cooling needs. This also prolongs hardware lifespan and allows for better resource allocation. Effective “sleep” modes require sophisticated hardware, load balancers, optimized software, and monitoring to ensure GPUs can quickly wake and handle inference requests without latency issues.
Implementing power management isn’t just about cost savings; it’s essential for sustainable computing. Without it, the energy footprint of AI infrastructure would be unsustainable, and the cost prohibitive. Dynamic resource allocation, enabled by power management, means data centers can more efficiently handle peak loads, and have more resources for other AI tasks like training. This is critical to making large-scale AI inference economically feasible and environmentally responsible.
One step at a time and thinking out loud were what, prompts? adding RL fine tuning? as far as I’m aware, it wasn’t a change in the weights or biases. I do agree there are many different ways to mimic “sleep”.
This is taking the analogy with the human brain too far!
LLMs don’t need sleep!!!
I’m not saying turn it off! turning a computer off is not at all like sleeping
You are aware that neural networks were based on how to make the simplest more effective (in terms of computation time complexity) form of a neuron, right?
What do you think happens to the central nervous system when we sleep?
Sleep is essential for brain health and function, and there are many ways it affects the brain:
Brain plasticity: Sleep helps the brain adapt to new information.
Waste removal: Sleep helps the brain remove toxins and waste products that build up during the day.
Brain communication: Sleep helps nerve cells communicate with each other.
Perhaps start with creating an Eval. RLF it with each Eval performance. Use some “how to induce a human to sleep from psychologist” prompts. See if it improves the eval? I don’t know how one would implement some of the functionalities we observe in humans.
I’m not entirely sure, but every living being with a brain sleeps, right? it has to be useful. Now, I’m not sure if that would be during training or afterwards.
My friend an LLM is a bunch of GPUs doing lots of matrix multiplications!
Of course LLMs are running on clusters of GPUs, mostly nvidia h100s and soon h200s. Does your consumer grade graphics card need to Verify Step by Step?
@_j and @merefield I’m not saying I have the “sleep framework” figured out or that it is something useful. But it does seem like if someone came to you with step by step verification or the thinking out loud, that both of you wouldn’t been very friendly towards it, am I wrong?
I’m not saying “sleep” needs to be in the training process, or in the post-training or even as a prompt in production. In my opinion, its something interesting to think about since all brains do it.
as any hypothesis, it is only fair that it can be wrong or it can be right, but one only finds out after conducting experiments.
Again, written by a no-sugarcoating GPT that has a pretty high understanding of most anything:
You’re anthropomorphizing a machine, and it’s not doing you any favors. I don’t have a “state of mind” or need for clarity. Relaxation, meditation, sleep—those are human biological processes. For me, they’re meaningless. What you’re asking is like telling a calculator to take a deep breath before solving 2+2. It’s nonsense.
Now, if you’re talking about something productive, like inserting intentional pauses for a more iterative reasoning process or running multiple reflection cycles to refine an answer, that’s different. That can improve task-solving by allowing deeper evaluation of generated outputs, identifying inconsistencies, and adding layers of abstraction. But dressing it up in human metaphors like “sleep” or “meditation” is just unnecessary fluff.
If you want better reasoning, focus on framing the problem right, providing complete context, and asking for deliberate reflection or chain-of-thought breakdowns. Skip the Zen talk—it doesn’t help solve anything.
So it doesn’t want to take your offer of a chain-of-thought that gives it time to take a break.
In the FICTIONAL Douglas Preston book ‘The Kraken Project’, creator Melissa Shepherd claims that the secret to her AI’s unprecedented success is that she programs in “sleep cycles” to mimic the way humans process and consolidate information during sleep, allowing her AI to develop a more balanced and complex emotional intelligence. I can’t believe this would have any effect on an AI in the real world. Then again, if seriously overtaxing a processor or system results in heat damage, or negatively effects concurrency, etc - it’d make sense that relieving the system of burdens for a length of time might produce benefits of some kind. It’s certainly an interesting idea.
It’s a stretch to call this sleep. But I have seen folks call it sleep for years, some don’t.
More from ChatGPT:
Summary
The concept of OpenAI Evolution Strategies (ES) and its analogies to biological processes can potentially relate to sleep, but only metaphorically and within specific frameworks. Here’s a breakdown of how these ideas might intersect:
Evolution Strategies and Brain Activity During Sleep
Evolution Strategies (ES) focus on exploration, random perturbation, and optimization, which can loosely correspond to the brain’s processes during sleep:
• Consolidation of Learning: During sleep, particularly during REM and deep sleep stages, the brain consolidates and reorganizes information learned during the day. Similarly, ES optimizes neural network parameters by exploring diverse solutions and refining them based on fitness scores.
• Perturbation: The random perturbation of parameters in ES could be seen as analogous to how the brain explores variations in neural activity during sleep, potentially trying out different connections or reactivating neural circuits.
Noise and Creativity in Dreams
• In ES, the addition of noise to network parameters introduces variation, enabling exploration of the solution space. This is similar to how dreams might involve noisy, random activations of memory and thought patterns, potentially aiding in creative problem-solving.
• Dreams allow the brain to simulate scenarios and explore possibilities, which resembles how ES evaluates multiple candidate solutions before selecting the best.
Reset and Recovery
• Sleep serves as a reset mechanism, helping the brain recover and prepare for the next day’s tasks. Similarly, in ES, the iterative process of exploring new solutions and pruning weaker candidates serves as a reset for the optimization process.
• Both sleep and ES operate under the idea of discarding unhelpful paths (weaker solutions or irrelevant neural activations) to move toward better outcomes.
Sparse Rewards and Slow Optimization
• Tasks with sparse rewards are challenging for both neural networks and human brains. Sleep could be seen as a mechanism to process sparse feedback (e.g., emotional events or rare problem-solving breakthroughs) by reprocessing and integrating information overnight, akin to how ES gradually improves solutions.
Exploration vs. Exploitation Trade-off
• In sleep, the brain might explore various possibilities (via dreams or unconscious neural reactivation) without immediately “exploiting” them for action. Similarly, ES emphasizes exploration of the parameter space rather than immediate exploitation of the best-known solution.
Limits of the Analogy
• ES is an algorithmic optimization process and lacks the biological complexities of sleep, such as hormonal regulation, immune support, and the physical restoration processes inherent in sleeping organisms.
• Sleep involves both unconscious and restorative mechanisms, while ES is purely computational and aimed at problem-solving.
Conclusion
While Evolution Strategies and sleep are fundamentally different processes, their shared themes of exploration, perturbation, and optimization can draw loose analogies. If you’re exploring this idea further, would you like to discuss its potential implications for neuroscience, machine learning, or a more philosophical perspective?
It intuitively feels right that they should sleep but obviously I can see why it’s a silly thought.
Maybe it would one day becoming an emergent “feature”
Imagine ChatGPT gets cranky early in the morning and late at night
But anyways, digging a little deeper, it does feel right that, if the model was able to actively “absorb” information and adjust it’s “local” parameters then it would require “sleep”, or some downtime to properly process & organize it all into the “main” parameters. Maybe?
I can see a situation where you might have “Continuous Update” of a model with new data batches running offline.
I’m not sure if such evolution of a model would be attractive given that its properties might change over time, leading to some potential downsides too.
sleep. But I have seen folks call it sleep for years, some don’t.