Overview
Registration Activity
Neuron registration trends
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −22.73%: protocol buys 0.00%, root sells −0.48%, users net −16.45%, cross-subnet −5.80%, unattributed −0.0008%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-03 UTC, as of ≈20:24 UTC (block 9,427,814)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Language model training requires generating high-quality training examples at the policy's learning frontier — prompts where different completions produce different rewards. Traditional approaches use reactive filtering: generate many rollouts, measure variance in outcomes, discard low-variance groups. This rejects compute-expensive data post-hoc. As models improve and frontier prompts become rarer, the rejection rate rises, wasting GPU resources.
A decentralized market transforms this compute loss into competitive incentive. Independent miners predict which prompts sit at the frontier, generate rollouts at their own cost, and submit candidates to a validator. The validator verifies that submissions match announced model weights and reward claims, selects frontier groups via cryptographic proof, and pays miners based on verified contribution. Miners who pick well earn emission; miners who pick poorly absorb their own compute cost.
Reliquary implements this on Bittensor subnet 81.
Miners run the current checkpoint, generate exactly 16 completions per prompt using a deterministic forced-sampling stream, construct GRAIL sketches binding each completion to hidden-state activations, and submit signed batches. The validator independently recomputes GRAIL sketches and rewards, admits groups via continuous FIFO proof, maintains one-shot prompt cooldown by canonical-content digest, and filters for in-zone reward variance (σ ≥ 0.24). Selected groups are accumulated, trained via GRPO after quarantine checks, and published as new checkpoints to Hugging Face Hub. This ex-ante prediction market structure maintains frontier diversity while eliminating the post-hoc discard tax.
Auto-generated from the GitHub repository and may not reflect the latest changes.
Reliquary is a decentralized GRPO training system on Bittensor subnet 81 where independent miners compete to contribute verified rollout groups from frontier prompts. The protocol uses GRAIL sketches for cryptographic proof of generation, validator-authoritative rewards, and a market-driven approach to dynamic prompt sampling, aiming to match DAPO's 50% training-step efficiency with a competitive ex-ante prediction model rather than post-hoc filtering.
Reliquary operates a decentralized training loop: miners generate rollouts on their own GPUs using the current checkpoint, construct GRAIL sketches binding completions to model activations, and submit signed batches to the validator. The validator admits groups via continuous proof (FIFO), verifies GRAIL sketches and rewards, maintains one-shot prompt cooldown via canonical-content digest, and filters for in-zone reward variance. Selected groups are bundled into training payloads, accumulated across a journal of 16-slot windows, and published as new checkpoints to HF Hub.
Miners poll /state for checkpoint changes and cooldown updates, receive provisional acceptance on submit, and earn EMA-smoothed fixed-slot shares once groups are proven and selected. The validator publishes training quarantine metadata for suspected poison and maintains auditable R2 archives of every window's submissions, verdicts, and metadata.
The zone filter (σ ≥ 0.24 for standard, σ ≥ 0.22 during bootstrap) selects groups with meaningful reward variance. Groups are ranked FIFO by upload arrival, payload metrics are telemetry-only. Each selected group earns one uniform fixed-slot share split across the environment count, picks target and B_BATCH (16).
The EMA smooths individual-window shares with α ≈ 0.027, giving ~25-window half-life. Per-epoch emissions are proportional to EMA score divided by the sum of all miner EMA scores. Miners are rejected for wrong checkpoint, bad termination, malformed proofs, GRAIL failures, prompt cooldown violations, out-of-zone σ, and fabricated completions.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
The RL layer of Bittensor
0
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Not yet verified: GPU model, VRAM, Notes
Not yet verified: GPU model, VRAM, GPU count, Notes
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 2 Nov 2026
Pull the pinned miner image (V1.0)
docker pull ghcr.io/reliquadotai/reliquary-validator:sha-3b3af1d-logic@sha256:7bb847499605308b1ad7b21f5430b1662eae506deacb6899d682bc0c11ce2742
Set environment variables for protocol v1 miner
export RELIQUARY_PROTOCOL_PROFILE=qwen3-4b-base-dapo-reliquary-v1 && export RELIQUARY_EXPERIMENTAL_FILL_CLOSED_ENABLED=1 && export RELIQUARY_TRAINING_RUN_ID=qwen3-4b-base-dapo-reliquary-v1-20260910
Start the miner with Math, Code and/or Logic environments
reliquary mine --network finney --netuid 81 --wallet-name YOUR_EXISTING_WALLET --hotkey YOUR_REGISTERED_HOTKEY --wallet-path YOUR_EXISTING_WALLET_DIRECTORY --validator-url http://62.238.81.36:8000 --environments openmathinstruct,opencodeinstruct,reliquary_logic_v2
Verify validator readiness before starting(optional)
curl --fail --silent --show-error --max-time 10 http://62.238.81.36:8000/readyz && curl --fail --silent --show-error --max-time 10 http://62.238.81.36:8000/state
Export the job contract for a specific evaluation task
reliquary tasks contract --task-id <job> > <job>.contract.json
Set task environment variables
export RELIQUARY_TASK_ID=<job> && export RELIQUARY_TASK_CONTRACT=$PWD/<job>.contract.json
Start the validator process for a specific task
reliquary validate --wallet-name <wallet> --hotkey <hotkey> --http-host 0.0.0.0 --http-port <port> --no-set-weights
Qualify proof capacity with measured end-to-end latencies (production v3+)(optional)
python scripts/qualify_proof_capacity.py staging-proofs.jsonl --output proof-capacity.json --software-revision <full-release-commit> --checkpoint-revision <full-benchmark-checkpoint-commit> --runtime-fingerprint-hash <health-runtime-profile-hash> --hardware-class 'NVIDIA H100 80GB HBM3' --benchmark-device-count <count> --measured-at 2026-07-31T00:00:00Z
Publish a stamped base checkpoint with lineage (production v3+)(optional)
python scripts/publish_base_reset_checkpoint.py
| Day (UTC) | Share |
|---|---|
| 2026-11-02 | 0.0021% |
| 2026-11-01 | 0.0027% |
| 2026-10-31 | 0.0016% |
| 2026-10-30 | 0.0038% |
| 2026-10-29 | 0.02% |
| 2026-10-28 | 0.0032% |
| 2026-10-27 | 0.0022% |
| 2026-10-26 | 0.02% |
| 2026-10-25 | 0.02% |
| 2026-10-24 | 0.01% |
| 2026-10-23 | 0.0031% |
| 2026-10-22 | 0.00% |
| 2026-10-21 | 0.0003% |
| 2026-10-20 | 0.0003% |
| 2026-10-19 | 0.00% |
| 2026-10-18 | 0.00% |
| 2026-10-17 | 0.0008% |
| 2026-10-16 | 0.0006% |
| 2026-10-15 | 0.00% |
| 2026-10-14 | 0.0004% |
| 2026-10-13 | 0.0016% |
| 2026-10-12 | 0.0022% |
| 2026-10-11 | 0.01% |
| 2026-10-10 | 0.01% |
| 2026-10-09 | 0.01% |
| 2026-10-08 | 0.0029% |
| 2026-10-07 | 0.0012% |
| 2026-10-06 | 0.0027% |
| 2026-10-05 | 0.0006% |
| 2026-10-04 | 0.0006% |
| 2026-10-03 | 0.0015% |
| 2026-10-02 | 0.0021% |
| 2026-10-01 | 0.0006% |
| 2026-09-30 | 0.0002% |
| 2026-09-29 | 0.0002% |
| 2026-09-28 | 0.0004% |
| 2026-09-27 | 0.0003% |
| 2026-09-26 | 0.0002% |
| 2026-09-25 | 0.0003% |
| 2026-09-24 | 0.0001% |
| 2026-09-23 | 0.0001% |
| 2026-09-22 | 0.0001% |
| 2026-09-21 | 0.0003% |
| 2026-09-20 | 0.0008% |
| 2026-09-19 | 0.0023% |
| 2026-09-18 | 0.01% |
| 2026-09-17 | 0.03% |
| 2026-09-16 | 0.11% |
| 2026-09-15 | 0.23% |
| 2026-09-14 | 0.25% |
| 2026-09-13 | 0.22% |
| 2026-09-12 | 0.20% |
| 2026-09-11 | 0.10% |
| 2026-09-10 | 0.18% |
| 2026-09-09 | 0.35% |
| 2026-09-08 | 0.35% |
| 2026-09-07 | 0.33% |
| 2026-09-06 | 0.37% |
| 2026-09-05 | 0.42% |
| 2026-09-04 | 0.42% |
| 2026-09-03 | 0.43% |
| 2026-09-02 | 0.39% |
| 2026-09-01 | 0.31% |
| 2026-08-31 | 0.31% |
| 2026-08-30 | 0.31% |
| 2026-08-29 | 0.33% |
| 2026-08-28 | 0.34% |
| 2026-08-27 | 0.33% |
| 2026-08-26 | 0.32% |
| 2026-08-25 | 0.32% |
| 2026-08-24 | 0.29% |
| 2026-08-23 | 0.27% |
| 2026-08-22 | 0.35% |
| 2026-08-21 | 0.42% |
| 2026-08-20 | 0.43% |
| 2026-08-19 | 0.43% |
| 2026-08-18 | 0.54% |
| 2026-08-17 | 0.59% |
| 2026-08-16 | 0.58% |
| 2026-08-15 | 0.56% |
| 2026-08-14 | 0.58% |
| 2026-08-13 | 0.57% |
| 2026-08-12 | 0.59% |
| 2026-08-11 | 0.62% |
| 2026-08-10 | 0.62% |
| 2026-08-09 | 0.60% |
| 2026-08-08 | 0.57% |
| 2026-08-07 | 0.55% |
| 2026-08-06 | 0.53% |
| 2026-08-05 | 0.56% |