This episode unpacks Templar's ambitious journey into incentivized, decentralized AI training, revealing the technical hurdles, community dynamics, and strategic potential of building large models permissionlessly.
Introducing Templar: The Vision for Decentralized AI Training
- The discussion kicks off with introductions, setting the stage for a deep dive into Templar, a BitTensor subnet focused on decentralized AI model training.
- Distributed (formalized.nets), the driving force behind Templar, invites key community members Joel and Evan to join the conversation, emphasizing the collaborative nature of the project from the outset.
The "Why": Countering Centralized Control and Copyright Concerns
- Distributed initially intended to skip the "why," but highlights a recent controversy involving Sam Altman allegedly violating copyright as a potent example of why decentralized alternatives are crucial. He argues passionately, “We need the ability to build these models otherwise people like Sam Altman do stuff like this.”
- Const (Jacob Steeves, Opentensor Foundation) strongly reinforces this point. He argues that if AI models inevitably learn from existing creative works (like art), decentralized, community-owned models are the only way to ensure the value generated is distributed fairly, rather than being captured solely by centralized entities.
- This establishes the core motivation: creating Foundation Models (large AI models trained on broad data, adaptable for various tasks) that are not controlled by a single entity, ensuring broader access and fairer economics.
How Templar Works: Incentivized Gradient Contribution
- Distributed outlines Templar's core mechanism: miners are assigned specific, deterministically random slices of a dataset ("pages") to train on within defined time windows (epochs, measured in BitTensor blocks – e.g., 7 blocks or ~84 seconds).
- Miners must train intensely on their assigned data during this window, generating gradients – mathematical updates indicating how the model's parameters should be adjusted to minimize errors (loss).
- These gradients are then uploaded to Cloudflare R2 buckets (a type of cloud object storage known for zero-cost data egress), making them accessible for validation. This choice is strategic for bandwidth and accessibility.
The Validator's Role: Ensuring Gradient Quality
- The validation process is critical for maintaining model integrity in a decentralized setting. Validators assess a miner's contribution by sampling the miner's assigned data pages.
- They perform a forward pass (calculating the model's output and loss on the sample), apply the miner's submitted gradient to their own copy of the model, run another forward pass, and measure the change in loss.
- A miner is rewarded based on how effectively their gradient reduces the loss compared to a baseline (initially, the validator's own effort, later refined based on miner feedback). Distributed notes the system is strict: “We slash a lot because the effects of fault... are so high.”
Challenges of Decentralized Training: Complexity and Incentives
- Const emphasizes the extreme difficulty of this endeavor. Training a model across a distributed network is inherently complex, but doing so with permissionless, potentially adversarial peers who are economically incentivized adds unprecedented challenges.
- He highlights Templar's groundbreaking nature: “What's core about Templar is the fact that you have incentivization directly implemented into the training run and I don't think anybody's ever done that before.” This integration of economic incentives directly into the training loop is Templar's key differentiator.
Lessons Learned: Hardship, Community, and Miner Exploits
- Distributed shares candid lessons from Templar's development, describing decentralized training as “hard on steroids” compared even to blockchain consensus mechanisms like BFT (Byzantine Fault Tolerance) – a system's resilience against failing or malicious components. Near-perfect miner uptime and correctness are demanded.
- A pivotal moment occurred when Const publicly shared Distributed's struggles, fostering vulnerability. This shifted Templar from a top-down project to a collaborative community effort, turning potential adversaries into partners invested in collective success.
- He details several miner exploits encountered:
- Gradient Norm Manipulation: Maliciously crafted gradients with large numerical values (norms) to disproportionately influence the aggregated model state (exploited over Christmas).
- Bucket Copying: Miners simply copying successful gradients from others' public R2 buckets instead of computing their own.
- Free Riding: Exploiting validation delays by submitting a few good gradients early in a window and then shutting down machines.
- These experiences underscore the relentless need for robust validation and incentive design in permissionless systems.
Iterative Development and Miner Incentives in BitTensor
- Const praises the BitTensor ecosystem's tooling (Python, Docker Watchtower) for enabling rapid iteration, allowing teams like Templar to test hypotheses and fix exploits directly in production – essential for adversarial environments.
- He observes that miners initially act as a "red team," constantly probing for weaknesses. However, BitTensor's tokenomics (Detail), where miners earn subnet tokens, transforms them into stakeholders. This aligns incentives, encouraging them to help secure and improve the network they co-own.
The Road Ahead: Optimizing, Scaling, and Future Models
- Distributed outlines Templar's future direction, focusing on optimization and scaling. Key challenges include reliably incorporating gradients from more miners beyond the current Top K (selecting the best K contributors based on performance) and understanding model saturation through ablation studies (systematically testing components to gauge their impact).
- The immediate goal is training a high-quality 1.2 billion parameter model, viewing mastery at this scale as crucial preparation for tackling larger models (e.g., 70 billion parameters).
- Longer-term, the team aims to contribute research findings and establish Templar as the leading platform for decentralized large model training (“Valhalla.”)
Live Run Analysis: Observing Decentralized Training in Action
- Distributed shares visualizations of a live training run, showcasing two views of the loss curve:
- Miner View: Extremely noisy, reflecting the inherent chaos of individual miners going offline, submitting errors, or being out of sync. Const remarks, “no training run in the world has this amount of variance between its workers.”
- Validator View: A much smoother, consistently decreasing loss curve, demonstrating the validation mechanism successfully filtering noise and aggregating valid contributions into a coherent global model.
- This visual evidence suggests the core mechanics are functioning and stabilizing.
Miner Perspectives: Competition and Optimization Frontiers
- Miners Noah and Daniel join the discussion, confirming the subnet has become highly competitive. The focus has shifted from finding simple exploits (“bugs”) to optimizing performance.
- Noah notes challenges like keeping miner models perfectly synced with the validator's global model, which can negate the benefits of advanced local training techniques. Key optimization areas now include training speed and efficient bandwidth usage for gradient uploads.
Technical Deep Dive: Synchronization, Validation, and R2 Buckets
- The conversation explores the critical challenge of synchronization. Inconsistent gradient submission times or miners failing to pull the latest updates can lead to divergence.
- Distributed explains implementing strict time boundaries (T-min/T-max) for gradient uploads to R2, ensuring all validators and participating miners work with a consistent set of gradients for each step.
- Const highlights the clever use of R2/S3 buckets, which provide globally verifiable, immutable timestamps linked to BitTensor block times. This acts as a decentralized clock, enabling enforceable deadlines – a primitive hard to achieve with standard peer-to-peer networking (Libp2p, Ethereal were considered and discarded). This focus opens the door for miners to compete on bandwidth and infrastructure quality.
The "Holy Grail" Revisited: Why Decentralized Training Matters
- Const passionately reiterates the grand vision: Templar represents a path towards the “holy grail” – enabling a globally distributed, permissionless community to pool resources and train AI models that can compete with those from tech giants (OpenAI) and state-backed labs (DeepSeek).
- He argues this is vital for creating truly open, transparent, and co-owned AI, moving beyond centralized control and permissioned collaborations towards a genuinely decentralized and incentivized ecosystem.
Investor Perspective: Significance and Future Incentive Design
- Joseph, providing a venture capital viewpoint, emphasizes the profound significance of Templar's permissionless, incentivized model. He contrasts it with numerous well-funded startups ($250M+ raised collectively) attempting permissioned decentralized training, highlighting Templar's unique, truly open approach.
- He poses a critical question: How will Templar's incentive mechanisms need to evolve to handle the increased complexity and higher stakes (larger bounties for exploits) associated with training larger, more valuable models?
Evolving Adversarial Landscape and Tooling Needs
- Distributed acknowledges the escalating challenge: as models become more valuable, adversarial attacks will likely become more sophisticated, potentially targeting the model's integrity (e.g., backdoor insertion) rather than just the incentive mechanism. The incentive landscape must continuously adapt.
- The discussion also touches on tooling. Distributed expresses a need for open-source alternatives to tools like Weights & Biases (W&B) for logging and analysis, potentially leveraging other BitTensor subnets (e.g., storage) to avoid reliance on centralized, potentially restrictive platforms. Versioning and provenance tracking for gradients are seen as future needs.
The Competitive Evolution of Subnets: From Exploits to Innovation
- Const theorizes that the next competitive edge for miners might lie in how they compute gradients – developing more efficient or advanced algorithms (e.g., higher-order methods) to reduce loss faster. Templar could become a marketplace for gradient generation techniques.
- Distributed aligns with this, describing a natural subnet lifecycle: initial phases focus on patching exploits; once stable, competition shifts to optimizing the core work (compute, bandwidth, algorithms), driving genuine innovation. Daniel's surprisingly low loss results hint this phase may be starting.
Concluding Thoughts: Open Ownership and the Future of Templar
- Const commends Distributed for his leadership, technical skill, and crucial embrace of humility and open ownership. He cites this approach as fundamental to building Templar's strong, collaborative community, where contributions are valued regardless of formal affiliation (using TAO Stats creator MogMachine and miner Noah as examples).
- The power of this open model, where the community collectively owns and improves the system, is presented as BitTensor's “superpower.” There's palpable excitement for Templar's potential to scale and train increasingly powerful models.
Strategic Conclusion
- Templar proves incentivized decentralized training works, moving beyond exploits to efficiency optimization. Investors/researchers must track its scaling and incentive evolution, as this model could disrupt centralized AI development and ownership paradigms, creating new opportunities for participation and investment in truly open AI infrastructure.
