Richard Sutton’s Oak Lab — New Algorithms for AGI Beyond Static LLM Training
Turing Award winner Richard Sutton co-founded Oak Lab (Toronto, Jul 2026) with Khurram Javed after Keen Technologies — OaK architecture, continual RL from experience, 20W trillion-parameter goal. explainx.ai maps the bet vs LLMs.
Reinforcement LearningAGIRichard SuttonOak LabAI ResearchContinual Learning
The Turing Award winner who wrote the RL textbook just opened a neolab betting against the LLM playbook.
In July 2026, Richard Sutton — 2024 ACM Turing Award laureate, co-author of Reinforcement Learning: An Introduction, and the researcher most associated with temporal-difference learning — co-founded Oak Lab in Toronto with Khurram Javed. The pair left John Carmack's Keen Technologies to pursue what Sutton calls "fundamentally new ideas" for AGI: agents that learn from runtime experience, not curated static datasets.
Reject current deep learning as sufficient foundation
Reinforcement learning central
New algorithms — not scaling existing DL stacks
AGI ambition
Alberta Plan / OaK roadmap vs Carmack's engineering sprint
Keen Technologies (Dallas) — Carmack's AGI bet, backers include Tobi Lütke — pursues human-level capability with heavy engineering. Sutton and Javed wanted to rethink learning machinery itself, not only ship faster on today's architectures.
explainx.ai read: This is the second high-profile RL-neolab fork narrative in 2026 — talent leaving well-funded AGI shops when algorithmic philosophy diverges, not only compensation.
As AI has become a huge industry, to an extent it has lost its way. What is needed to get us back on track to true intelligence? Most of all we need agents that learn continually from their first-person experience.
Three structural bets
Everything learns continually — no frozen pretrained backbone with a thin finetune head
Learning from experience instead of curated datasets
Jul 13, 2026
First Oak Lab publication
Event-driven NNs, batch-size-one learning
Coming soon
Listed on site
Thread replies praised Sutton's NeurIPS OAK talk — the lab productizes a ** decade-long Alberta Plan**, not a weekend manifesto.
The anti-LLM pitch — imitation vs evaluation
Sutton's June 2026 public framing (via press summaries): generative AI imitates but cannot evaluate its own outputs — blocking discovery.
LLM industry loop (2026)
Oak Lab counter
Pretrain on curated web/code
Learn online from agent's own stream
RLHF/DPO on human/AI preferences
Reward from environment + internal critique
Frozen weights + periodic retrain
Continual weight + step-size updates
World as text
World models + planning
That does not mean Oak ignores OpenAI's beneficial-trait RL results — it reframes them as patching imitation systems rather than building experience-native agents.
Research AGI foundations → ask whether online RL + world models escape dataset ceiling and catastrophic forgetting
20 watts, trillion parameters — literal or lodestar?
Press reports Sutton's long-term goal: an agent with ~1 trillion parameters that learns and plans in real time on ~20 watts.
Context
Scale
Human brain
~20W — often cited in AGI rhetoric
Single H100
~700W — runs models orders smaller than 1T interactively
Frontier LLM training
Megawatts across clusters
Plain read: The number is a north star for efficiency + continual learning, not a 2026 product spec. Oak Lab is an algorithm lab first — hardware co-design may follow (Keen DNA) but is not the launch headline.
Bitter Lesson tension — thread FAQ answered
Neolab skeptics on X asked: Doesn't Sutton's own Bitter Lesson say scale + general methods beat hand-crafted algorithms?
Bitter Lesson core
Oak Lab response (implicit)
General methods win
Agree — but static backprop on fixed datasets may not be the winning general method for continual agents
Compute + data scale
Oak bets experience streams are the data — efficiency matters (20W)
Human knowledge hurts
OaK builds abstractions online (FC-STOMP) instead of hand-engineering features
Sutton is not anti-scale — he is anti-"scale the wrong paradigm forever." Oak Lab is a neolab bet that 2020s DL is the new 2010s chess engines: impressive, industry-defining, not the final AGI substrate.
Social acquihire jokes (Anthropic buys Oak in months) reflect RL talent scarcity — Sutton is the canonical RL citation — not a disclosed deal.
Toronto vs Alberta: Riley RostovM and others noted Oak is not in Edmonton (Amii / historical Sutton base) or Vancouver — but Canada retains the research gravity Sutton built at University of Alberta for decades.
Who should care
Audience
Takeaway
RL researchers
Watch oaklab.ai for batch-size-one / event-driven posts
LLM product teams
Oak does not invalidate your stack — it questions 10-year substrate
Investors
Neolab thesis diversity — not every AGI bet is more transformers
Policy / safety
Continual agents raise new monitoring problems — static eval harnesses may not transfer
Summary
Richard Sutton co-founded Oak Lab in Toronto (July 2026) with Khurram Javed after leaving Keen Technologies — pursuing new AGI algorithms built on reinforcement learning from experience, the OaK architecture, and continual world models, not curated dataset scaling. The lab's first note dropped July 13; Sutton's moonshot remains a ~1T-parameter, ~20W, real-time planning agent. Social hype (~94K views) frames a neolab moment; the substance is a direct challenge to LLM pretrain + RLHF as the final path to intelligence — from the researcher who literally wrote the book on RL.
Oak Lab claims and Sutton quotes reflect July 2026 public announcements. AGI timelines and hardware targets are aspirational — not verified product commitments.
