[P0] Training on Gonka #1201
up-for-grabs Priority: High
WHAT DOES TRAINING ON GONKA MEAN?
Being able to train frontier-level models is an important long-term goal to make AI fully independent of centralized datacenters.
What decentralized training implies:
- Geo-distributed
- Trustless
- With no single point of control
BASIC PRINCIPLES
- Pluralism: Do not to lock the network in on a single team or a single approach
- Anyone can propose their approach
- Community reviews it approves/rejects
- Many approaches and training runs can run at the same time
- Isolation: Training happens in separate self-contained shards based on simple primitives
- Heavy data transfers and all the training coordinations doesn’t mess with the main chain
- Gradualism: Training infrastructure develops gradually as more runs are made and different methods are being established as successful ones.
- Different validation, optimization, communication mechanisms can be tested inside the shards
- The effective ones can be reused and established in the infrastructure
INFRASTRUCTURE LEVELS
- Shards primitives: open shard, settle shard — prioritized
- Training primitives: save/exchange artifacts, allreduce, evaluate checkpoint — prioritized
- Post-training tools: generate synth data/rollout traces, GRPO loss
- RL environments: separate agent containers
- Scaling: research on how to scale everything up
Source: https://docs.google.com/presentation/d/1dX26zZLWAlLqdRylKQ5FYIZTlsgEt3ZJKKmw5_5PSxw/edit?slide=id.g380f8445137_1_0#slide=id.g380f8445137_1_0
Discussed on GIP: https://discord.com/channels/1336477374442770503/1415622117629624362/1500920059936116807
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