Incentivizing Intelligence: The Bittensor Approach
Jacob Steeves1∗

Ala Shaabana1∗

Sin Tai Liu1
1

Yuqian Hu1

Francois Luus1

Jacqueline Dawn Tasker-Steeves1
Opentensor Foundation

Abstract
Inspired by the efficiency of financial markets, we propose that a market system
can be used to effectively produce machine intelligence. This paper introduces amechanism in which machine intelligence is valued by other intelligence systems
peer-to-peer across the internet. Peers rank each other by training neural networks
that are able to learn the value of their neighbours, while scores accumulate ona digital ledger. High-ranking peers are rewarded with additional weight in the
network. In addition, the network features an incentive mechanism designed toresist collusion. The result is a collectively run machine intelligence market that
continually produces newly trained models and rewards participants who contributeinformation-theoretic value to the system.

1

Introduction

The production of machine intelligence relies almost exclusively on a system of benchmarking, inwhich a model’s performance is assessed on narrowly defined supervised problems. While thismethod works well for task-specific problems, measuring machine intelligence solely with supervised
objectives causes the field to converge towards narrow specialists, as opposed to resilient generalists(Radford et al. [2019]).
In 2012, an inflection point occurred when AlexNet achieved state-of-the-art performance on the ImageNet competition (Krizhevsky et al. [2017]). This triggered a sharp acceleration in the developmentof new neural network approaches able to overcome purely statistical methods with high dimensional,
representation-based approaches. Only a few years later, Vaswani et al. introduced the first-ever unsupervised learning method that would surpass supervised learning regimes in language understanding(Vaswani et al. [2017]). These innovations demonstrate that researchers can achieve state of the art
results by making consistent, incremental improvements upon previous work, consistently adding toa shared base of research over time.
However, since intelligence produced by these models is always lost, this approach is quite inefficient.
Users have to retrain models on their own systems to replicate, or improve upon, the work of others.
Consequently, this leads to unnecessary computational loss in learning tasks that other models havealready learned.
Additionally, there is the issue of model evaluation. Presently, new research must go through theacademic peer review process to investigate the work while ensuring quality and correctness. While
this process has its benefits, it creates a system that is unscalable and subject to human bias. It isnotoriously difficult to reproduce artificial intelligence research Gundersen and Kjensmo [2018].
There must be a more efficient and objective method to evaluate AI model performance.
In this paper, we introduce the Bittensor Network: a decentralized peer-to-peer machine learning
protocol. In the network, machine intelligence is measured by other intelligence systems in acontinuous and asynchronous peer-to-peer (P2P) fashion across the internet. Models are ranked for
informational production regardless of the subjective task or dataset used to train them. By changing
2022 Trustworthy and Socially Responsible Machine Learning (TSRML 2022) co-located with NeurIPS 2022.

Figure 1: Schematic of the Bittensor Network
the basis against which machine intelligence is measured, the market can reward intelligence that is
applicable to a much larger set of objectives while still preserving the unique value of legacy systems
and smaller diverse systems that find niches within the reward landscape. The constructed market
uses a digital ledger to record ranks and to provide incentives to the researchers, thus allowing themto directly monetize their machine intelligence work. It is divided into two layers: the AI layer andthe Blockchain layer (Figure 1).

2

AI layer

In addition to inference and training, the AI layer is responsible for abstracting away the Bittensor
kernel and ensuring input/output compatibility of a node’s neural network with the rest of the
network’s peers. Each node on the Bittensor Protocol contains a single neural network. From now on,
we will be referring to these nodes as neurons; this is not to be confused with individual neurons in
traditional neural networks.
The Bittensor Protocol is composed of n parameterized functions (or neurons) F = f0 , ..., fj , ...fn
distributed in a peer-to-peer fashion. Each neuron is holding zero or more network weights S = [si ](or “stake”) represented on a digital ledger. Our goal is the distribution of stake I – as incentive – topeers who have contributed the most informational value.
Figure 2 shows a high level overview of the forward and backward passes performed on the Bittensor
network and describes the process by which neurons train on the Bittensor protocol. Similarly to
the Hivemind proposal by Ryabinin et al., peers are able to request others for forward passes andsubsequently propagate backward gradients over the wire. By querying for information, Bittensor
offers inference across all of the nodes serving on the network. Unique to Bittensor, this enables an
distributed trust-less style of machine learning where requests are authenticated and verified using thedigital ledger. The Blockchain layer ensures the identity of each peer through a unique cryptographic
key and maintains the integrity of incentive distribution and rankings over the entire network.
In Bittensor, peers who are highly ranked receive the most incentive. Peers use the outputs of others
F (x) = [f0 (x)...fn (x)] as inputs to themselves f (F (x)) and learn a set of weights W = [wi,j ]where peer i is responsible for setting the ith row through transactions on a digital ledger. We describe
peer ranking in more detail in Section 4.
Figure 3 shows a cutaway diagram of two neurons on the Bittensor network. Synapses function as themain transmitter between neurons and ensure all of the transmission tensors are formatted in correspondence to the Bittensor protocol. Each synapse dictates a different ML task, and therefore follows
a different compression and communication process. This ensures that all logits and embeddings
being transported over the Bittensor protocol are sanitized, correct, and contribute useful information.
If a neuron is acting maliciously or sending incorrect tensors across the wire, Synapses ensure that
their rankings and rewards are penalized as a result. Currently, the Bittensor network is focused onlarge language modelling and contains 4 separate synapses for text-related tasks ( LastHiddenState,2

Figure 2: Neurons training on the Bittensor Protocol. The Bittensor protocol utilizes a mixture-ofexperts (MoE) architecture with each remote peer acting as a single expert(Shazeer et al. [2017];
Ryabinin and Gusev [2020])

Figure 3: Schematic diagram of a sample neuron on the Bittensor network. Models query each otherover the wire and use the response as part of its own model. Responses are then evaluated for theirinformational value and ranked based on their contribution to the collective. Responses and gradients
can travel over the wire as the network trains.
CausalLM, CausalLMNext, and Seq2Seq). Prior works have shown that large language models can becontinually fine-tuned for improved performance for downstream tasks (Scialom et al. [2022],Ouyang
et al. [2022]). The Bittensor protocol achieves the same continual fine-tuning process while being
fully distributed and trust-less over the web.

3

Blockchain Layer

The Blockchain layer is a layer-0 blockchain based on Polkadot Substrate (Wood [2016]), and is
responsible for enforcing the consensus mechanism, ensuring peer identity, and incentivizing networkpeers. The blockchain layer resides directly underneath the AI layer, and communication between the
two layers is achieved through inter-process communication. In order to fairly distribute incentive
across all of the participating peers, the Bittensor network leverages a staked weighted trust throughconsensus. Peers are incentivized to rank each other, and highly ranked peers will receive additional
rewards. The blockchain does not trust rankings from any individual peer on the network, but rather
trusts the collective rankings from all of the participating peers. In order to submit their rankings,
peers must first register their wallets, and a pair of cryptographic keys that are unique to each peer.
3

The same keys will be required to sign all transactions to the chain and all communication requests
between peers. These cryptographic keys will serve as the main identification tool for peers on the
network.
Additionally, the blockchain maintains the system’s incentive mechanism, designed primarily forpeer-rankings systems and to solve for the problem of collusion, which occurs when a small fractionof the network works selfishly together as a means to maximize their rewards.
To solve this problem, we have leveraged a trust based incentive mechanism by rewarding peers in
the network who are “trusted” and have reached consensus. The incentive mechanism utilizes a stake
vector S and a set of weights W where rows are inter-peer rankings. We also infer a trust matrix T
from the weights, where ti,j = 1 if and only if there is a non-zero edge between peer i and j.

w0,0
w1,0
W=
w2,0
w3,0

w0,1
w1,1
w2,1
w3,1

w0,2
w1,2
w2,2
w3,2


w0,3
w1,3 
w2,3 
w3,3

 
s0
s1 
S= 
s2
s3


t0,0
t1,0
T=
t2,0
t3,0

t0,1
t1,1
t2,1
t3,1

t0,2
t1,2
t2,2
t3,2


t0,3
t1,3 
t2,3 
t3,3(1)

We define peers who have reached “consensus” as those with a positive weight settings from morethan 50 percent of stake (T > 0.5) in the network. Hence, when more than 50% of the incentive
goes to consensus peers, then the chain can be considered as having reached consensus. This isregularized on the chain by the sigmoid function defined by Equation 2 such that peers who havereached consensus receive significantly higher rewards. Effectively, the threshold-like sigmoid
rewards connected peers and punishes the untrustworthy peers.
C = σ((TT S − 0.5))(2)

We use the consensus term to scale the original rankings. This ensures the majority of the incentive isdistributed to peers that trusted by a majority of the network.
I=R·C

(3)

To give an example of how this might work in practice, consider a scenario in which a colluding
cabal C has formed on the network Figure 4. The cabal will actively try to maximize their inflation
by only ranking themselves highly while providing no informational knowledge for the honest H
peers on the network. In this disconnected network, the two subgroups will only vote for peers in its
own group with little to no weights for peers of the opposite group (Figure 4). Assuming that the
chain has reached consensus, the fight between honest H peers and cabal C peers can be determinedby the portion of initial stake each sub-group contains.

Figure 4: Disjointed sub-networks: Cabal peers (left) only vote within its own group and does notinteract with a majority of the peers and stake on the network. This results a lower consensus C score
for the sub-network and thus a lower incentive.
4

In the event that the stake in the honest sub-graph is greater than the cabal (SH > SC ), only peers
within the honest subgroup are capable of reaching consensus and will receive the majority of the
incentive from the chain. The disagreement between the majority stake holders SH , and the dishonest
sub-graph results in a penalized consensus and therefore incentive for the C sub-graph. As such, theCratio of stake SCS+S
decreases over time, and the cabal must continually add stake to maintain its
Hposition. These cabal attacks are akin to the 51% attacks present in other blockchains (Sayeed andMarco-Gisbert [2019]). However, our stake based ranking system – like other proof of stake systems– present significantly higher risks for the attackers that endanger the value of their own tokens.
Additionally, our incentive mechanism includes a bonding mechanism (Appendix Section 9) toprovide additional incentives for peers who validate honestly. Similar to market-based speculation in
traditional equities, the peers that have accumulated bonds in peers that others will later value attain
increased inflation themselves.

4

Game theoretic scoring

A crucial element of the Bittensor Protocol is the production of an accurate ranking method. Peers
are ranked based on their informational significance to the overall collective, represented by a ranking
r = [ri ] where score ri ∈ R represents a participant’s contribution to the benchmark. This is done bycalculating its Shapley value ϕ (Shapley [1952]) which is a way to fairly distribute the contribution
for each participant in a group by permuting through all possible combinations of the subsets:
ϕi =

1
|N |!

X

|S|!(|N | − |S| − 1)![CE(S ∪ i) − CE(S)](4)

S⊆N \{i}where N and S represent the complete set and a single set of peers respectively. CE(S) is the
cross-entropy loss give S set of peers, ϕi represents the Shapley value for a peer i.

Figure 5: Shapley score comparison between large language models (LLM) of various sizes (GPT
2 — 117M, GPT-neo — 125M, GPT-J — 6B, GPT-neox — 30B) (Wang and Komatsuzaki [2021] ,Black et al. [2022]) by a single validator. The scoring was done on the Bittensor network, and models
were scored based on their performance at last token prediction. Larger models capable of achieving
lower loss are given a higher Shapley score by their peer.
In order to calculate the Shapley score for thousands of peers, the network leverages a sparse Monte
Carlo estimation method. In terms of the AI layer, this is akin to a sparsely gated mixture-of-experts(MoE) layer (Shazeer et al. [2017], Fedus et al. [2021]) such that only a portion of the network is
queried at a given time. This was implemented to allow the network to scale efficiently without facing
any computational bottlenecks.
G(x) =X

gj (x) ∗ fj (x)(5)

jwhere gj (x) represents the output of the gating network and fj (x) represents the response from peer,
j. Unlike previous MoE models where routing determines the parameters to activate, our gating layer
determines the optimal peers in the network to query for each example. Responses are then used as
the inputs as part of a local model and evaluated for their contribution. This cuts outward bandwidth5

by querying only a small subset of peers for each example and thus also reducing the number ofpossible permutations in our Shapley calculation.
The sparse combinations of peers effectively represent a subset of the possible permutations within
the complete set of N peers. Given enough samples, we can recover the full Shapley values and acooperative ranking r for the entire network. Benchmarks of several LLMs currently running on the
Bittensor network are showcased in Figure 5.

5

Current State of The Bittensor Network

The Bittensor network officially launched in November 2021. As of writing this paper, the network
contains 4096 peers; all of which are running a language model that is actively querying others for
information or serving requests from other peers on the network. With a total sum of 500 Billion
parameters running across 4096 peers, it is possible to query and infer from every node in the
network and receive an output of logits, embeddings, or plain text responses given a prompt. Eachpeer is either running a custom language model, or a pre-trained model such as GPT-J (Wang andKomatsuzaki [2021]), GPT-Neo, (Black et al. [2021]), or GPT-2 (Radford et al. [2019]) downloaded
from HuggingFace API (Wolf et al. [2020]).
In 2022, the protocol underwent several algorithmic, consensus, and infrastructure changes to ensure
correctness and scalability. The Bittensor team is now training more sophisticated models directly on
the Bittensor protocol to work towards achieving performance comparable to current state of the art
models.

6

Conclusion

We have introduced the first peer-to-peer, trust-less intelligence market that features a novel ranking
benchmark. The benchmark measures performance as representational-knowledge production using
other intelligence systems to determine its value. Due to its high-resolution, highly collaborative
nature, this benchmark has the potential to be the gold standard in machine learning testing andevaluation.
The paper began with the definition of the Bittensor Protocol, a P2P network composed of abstractly
defined intelligence models. Peers are able to directly query each other for informational embeddingsthat serve as inputs to their own models. Training can occur by subsequently propagating backward
gradients to responsive peers. We then showed how this framework allows us to produce a cooperativegame-theoretic ranking for each peer based on its informational value for the network. These scoresare passed on to a digital ledger where a trust-based incentivization is distributed. Peers that earnhigh scores from a majority of stake holders will receive the majority of the incentive. We thendemonstrated how this incentive scheme based on peer connectivity prevents participants fromforming dishonest sub-graphs such that over time, dishonest sub-graphs decay into irrelevance.
The result of these various novel mechanisms is an intelligence market that rewards participants
for producing knowledge while enabling the sharing of this knowledge for new learners in thesystem. Similar to financial and other Web3 markets, the market for intelligence actively encourages
innovations among its participants and rewards for those who manage to provide unique knowledge tothe collective. In terms of ML, Bittensor provides incentives for participants to consistently improveand finetune their models over the network. This combination of Web3 and ML introduces an unique
network where a decentralized global contribution of compute can be put towards the advancementof machine intelligence.

References
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is all you need,” Advances in neural information processing systems, vol. 30, 2017.

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O. E. Gundersen and S. Kjensmo, “State of the art: Reproducibility in artificial intelligence,” in Proceedings of
the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
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[Online]. Available: https://arxiv.org/abs/2205.12393
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[Online]. Available: https://www.mdpi.com/2076-3417/9/9/1788
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M. Pieler, U. S. Prashanth, S. Purohit, L. Reynolds, J. Tow, B. Wang, and S. Weinbach, “Gpt-neox-20b: An
open-source autoregressive language model,” 2022. [Online]. Available: https://arxiv.org/abs/2204.06745
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Modeling with Mesh-Tensorflow,” Mar. 2021. [Online]. Available: https://doi.org/10.5281/zenodo.5297715
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7

Appendix8

Sample Rankings
Model Parameters

Bittensor Ranking

gpt-2
117M
0.0000106
gpt-neo
125M
0.0001242
bloom
3B
0.0002789
OPT
6.7B
0.0004604
gpt-J
6B
0.0005235
Table 1: Chain wide ranking comparison of various large language models currently being servedon the Bittensor network. The final rankings are computed based on the Bittensor protocol whereindividual peer rankings are submitted to the digital ledger. These rankings are normalized such thatthe total rankings of the 4096 peers sum to 1.

7

9

Bonds

Although the mechanism described in section 3 protects against collusion by making it difficult for small
groups to achieve inflation, it does not provide an incentive for correctly selecting weights. We introduce theseincentives by adapting the inflation mechanism with a speculation-based reward in the form of bonds B. Here,bi,j ∈ B is the proportion of bonds owned by peer i in peer j.
Bt+1 = Bt + W · S(6)Using the bond matrix B, the chain redistributes the normal incentive scores ∆S = BT · I. Like market-based
speculation on traditional equities, peers that have accumulated bonds in other peers that others will later value,
will attain increased inflation themselves. Thus if a group of peers with stake in the system are accumulating
bonds on a given peer that they expect to do well, then other peers are motivated to accumulate bonds as well tothis same peer – speculating on their future value.
Finally, we adapt this mechanism slightly to ensure peers attain a fixed proportion of their personal inflation. For
instance, 50 percent, ∆S = 0.5BT I + 0.5I. ∆S becomes the mechanism step update with determines network
incentives across the n peers.
St+1 = St + τ ∆S

10

Anatomy

10.1

Axons

(7)

Axons represent the server of a model that a registered account can server. The axon represents a gRPC server
and uses thread pools for handling multiple requests from dendrites (clients). The requests from clients areplaced in a priority queue . The priority score can bed defined based on the server’s purpose. The server connectswith the client via Universal Plug and Play protocol.

10.2

Dendrites

Receptors serve as client thread-pools when querying another server with inputs. The receptor uses multithreading pool executors for managing multiple IO bound connections with each of the server neurons. Requests
are signed by the client’s hotkey to ensure the account is registered with the Bittensor network. This excludes
accounts that are not registered to the Bittensor network.

10.3

SynapseA synapse is a schema of communication between a neurons. Synapse schemes ensure the client and server areagreeing on their messages, which is involved both for forward and backward pass messages. At the moment,
there consists of the following Synapses: LastHiddenState, CausalLM, CausalLMNext, and Seq2Seq.

10.3.1

LastHiddenStateThe LastHiddenState synapse is designed for communication of embeddings over the wire. A peer may request
for this synapse when requesting for a forward pass from another peer. The peer is then expected to process the
inputs and return its last hidden state before decoding over the wire.

10.3.2

CausalLM, CausalLMNext

Similar to the LastHiddenState synapse, both the CausalLM and CausalLMNext synapse is responsible for thetransport of logits over the wire. With the CausalLM synapse, peers will respond with the standard next token
prediction over all of the inputs, while with CausalLMNext synapse demand the predictions for the next token
following the input text.

10.3.3

Seq2Seq

With the Seq2Seq synapse, peers query other peers with the expectation of text generation given a input prompt.
Unlike the other synapses which are often used for evaluation of individual peers, the Seq2Seq synapse is mainly
used for extracting value out of the peers currently serving on the Bittensor network.

8