tomevault-io GFlowNet Skill
"Sample x with probability proportional to R(x), not just maximize R(x)." — Yoshua Bengio
Overview
GFlowNets (Generative Flow Networks) are a new paradigm:
- RL: Maximize expected reward → single optimal solution
- MCMC: Sample from distribution → slow mixing
- GFlowNet: Learn to sample P(x) ∝ R(x) → fast, diverse sampling
Core Concept
GFlowNet Objective:
∀ terminal state x: P_θ(x) = R(x) / Z
Where:
P_θ(x) = probability of generating x via forward policy
R(x) = unnormalized reward function
Z = partition function (normalizing constant)
Key Insight: We DON'T need to know Z to train!
Architecture
┌─────────────────────────────────────────────────────┐
│ GFlowNet │
├─────────────────────────────────────────────────────┤
│ Initial State s₀ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Forward │ P_F(s' | s) = learned policy │
│ │ Policy │ │
│ └──────┬──────┘ │
│ │ sample action │
│ ▼ │
│ ┌─────────────┐ │
│ │ Transition │ s → s' │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Terminal? │───No──▶ continue │
│ └──────┬──────┘ │
│ │ Yes │
│ ▼ │
│ ┌─────────────┐ │
│ │ R(x) │ Evaluate reward │
│ └─────────────┘ │
└─────────────────────────────────────────────────────┘
Training Objectives
1. Trajectory Balance (TB)
def trajectory_balance_loss(trajectory: List[State], reward: float) -> Tensor:
"""
TB: Z × Π P_F(s_t → s_{t+1}) = R(x) × Π P_B(s_{t+1} → s_t)
In log space:
log Z + Σ log P_F = log R + Σ log P_B
"""
log_Z = self.log_Z # Learnable parameter
log_P_F = sum(self.forward_policy.log_prob(s, s_next)
for s, s_next in zip(trajectory[:-1], trajectory[1:]))
log_P_B = sum(self.backward_policy.log_prob(s_next, s)
for s, s_next in zip(trajectory[:-1], trajectory[1:]))
loss = (log_Z + log_P_F - torch.log(reward) - log_P_B) ** 2
return loss
2. Detailed Balance (DB)
def detailed_balance_loss(s: State, s_next: State, reward_s: float) -> Tensor:
"""
DB: F(s) × P_F(s → s') = F(s') × P_B(s' → s)
Where F(s) = learned flow function.
"""
log_F_s = self.flow_network(s)
log_F_s_next = self.flow_network(s_next)
log_P_F = self.forward_policy.log_prob(s, s_next)
log_P_B = self.backward_policy.log_prob(s_next, s)
loss = (log_F_s + log_P_F - log_F_s_next - log_P_B) ** 2
return loss
Applications
1. Molecule Design
# GFlowNet for drug discovery
class MoleculeGFlowNet:
def __init__(self):
self.action_space = ['add_atom', 'add_bond', 'terminate']
def sample_molecule(self) -> SMILES:
state = EmptyMolecule()
while not state.is_terminal():
action = self.forward_policy.sample(state)
state = state.apply(action)
return state.to_smiles()
def reward(self, molecule: SMILES) -> float:
# Combines: drug-likeness, binding affinity, synthesizability
return docking_score(molecule) * qed(molecule)
2. Causal Discovery
# GFlowNet for DAG sampling
class CausalDAGGFlowNet:
def __init__(self, n_variables: int):
self.n = n_variables
def sample_dag(self) -> DAG:
"""Sample DAG with P(G) ∝ P(data | G)."""
dag = EmptyDAG(self.n)
while not dag.is_complete():
edge = self.forward_policy.sample(dag)
if not dag.would_create_cycle(edge):
dag.add_edge(edge)
return dag
3. Combinatorial Optimization
# GFlowNet for set generation
class SetGFlowNet:
def sample_set(self, universe: Set) -> Set:
"""Sample set S with P(S) ∝ R(S)."""
current_set = set()
for element in self.ordering(universe):
include = self.forward_policy.sample(current_set, element)
if include:
current_set.add(element)
return current_set
GF(3) Triads
# Causal-Categorical Triad
sheaf-cohomology (-1) ⊗ cognitive-superposition (0) ⊗ gflownet (+1) = 0 ✓
# Diversity Triad
persistent-homology (-1) ⊗ glass-bead-game (0) ⊗ gflownet (+1) = 0 ✓
# Sampling Triad
three-match (-1) ⊗ epistemic-arbitrage (0) ⊗ gflownet (+1) = 0 ✓
Integration with Interaction Entropy
module GFlowNet
def self.sample_proportional(candidates, reward_fn, seed)
gen = SplitMixTernary::Generator.new(seed)
# Build forward trajectory
trajectory = []
state = initial_state
until terminal?(state)
# Use color to guide sampling
color = gen.next_color
action = select_action(state, color)
next_state = transition(state, action)
trajectory << { state: state, action: action, color: color }
state = next_state
end
reward = reward_fn.call(state)
{
terminal_state: state,
reward: reward,
trajectory: trajectory,
trit: 1 # Generator (creates diverse samples)
}
end
end
Key Properties
- Amortized: Learn once, sample many times (unlike MCMC per-problem)
- Off-policy: Can train on any trajectories
- Diverse: Samples cover modes proportionally to reward
- Compositional: Build complex objects step-by-step
References
- Bengio, E. et al. (2021). "Flow Network Based Generative Models for Non-Iterative Diverse Candidate Generation."
- Malkin, N. et al. (2022). "Trajectory Balance: Improved Credit Assignment in GFlowNets."
- Deleu, T. et al. (2022). "Bayesian Structure Learning with Generative Flow Networks."
- torchgfn library
Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
Graph Theory
- networkx [○] via bicomodule
- Universal graph hub
Bibliography References
dynamical-systems: 41 citations in bib.duckdb
Cat# Integration
This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
GF(3) Naturality
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.
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