swirl-self-improving-world-models

name	swirl-self-improving-world-models
title	Self-Improving World Modelling with Latent Actions
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2602.06130
keywords	["World Models","Self-Improvement","Latent Actions","Inverse Dynamics","Unlabeled Data"]
description	Enable VLMs and LLMs to self-improve at world modeling by treating actions as latent variables and reciprocally optimizing forward and inverse dynamics models using only unlabeled state transitions.

Self-Improving World Modelling with Latent Actions

Problem Context

Foundation models (LLMs and VLMs) struggle with world modeling because current approaches require costly action-labeled trajectories. Collecting manual annotations for every state transition in open-world tasks is prohibitively expensive and intractable. Moreover, inverse dynamics (inferring actions from state transitions) is inherently ambiguous—purely supervised learning becomes brittle when data is sparse.

Core Concept

SWIRL treats actions as [latent variables, reciprocal optimization, unlabeled data] to enable self-improvement without annotations. The approach alternates between optimizing the Forward World Model (FWM) to predict identifiable states and optimizing the Inverse Dynamics Model (IDM) to infer plausible actions. This mutual-information maximization enables both models to improve from unlabeled state-only sequences.

Architecture Overview

• Phase I: FWM optimization maximizing state identifiability (conditional MI)
• Phase II: IDM optimization maximizing action inference (ELBO)
• Reciprocal loop: Each phase provides learning signal for the other
• Latent action framework: Actions inferred from transitions, not labeled
• GRPO training: Both models optimized via reinforcement learning with self-generated rewards

Implementation

Step 1: Define forward world model and inverse dynamics model

Implement both model components that cooperatively learn.

# Forward World Model (FWM)
class ForwardWorldModel(torch.nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super().__init__()
        self.encoder = torch.nn.Linear(state_dim, hidden_dim)
        self.action_encoder = torch.nn.Linear(action_dim, hidden_dim)
        self.predictor = torch.nn.Sequential(
            torch.nn.Linear(hidden_dim * 2, hidden_dim),
            torch.nn.ReLU(),
            torch.nn.Linear(hidden_dim, state_dim)
        )

    def forward(self, state, action):
        """Predict next state given state and action."""
        state_emb = self.encoder(state)
        action_emb = self.action_encoder(action)
        combined = torch.cat([state_emb, action_emb], dim=-1)
        next_state = self.predictor(combined)
        return next_state

# Inverse Dynamics Model (IDM)
class InverseDynamicsModel(torch.nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super().__init__()
        self.encoder = torch.nn.Linear(state_dim * 2, hidden_dim)  # (state, next_state)
        self.action_decoder = torch.nn.Sequential(
            torch.nn.Linear(hidden_dim, hidden_dim),
            torch.nn.ReLU(),
            torch.nn.Linear(hidden_dim, action_dim)
        )
        self.log_std = torch.nn.Parameter(torch.zeros(action_dim))

    def forward():
        
        combined = torch.cat([state, next_state], dim=-)
        encoding = .encoder(combined)
        action_mean = .action_decoder(encoding)
        action_log_std = .log_std
         action_mean, action_log_std

     ():
        
        action_mean, action_log_std = .forward(state, next_state)
        action_std = torch.exp(action_log_std)
        dist = torch.distributions.Normal(action_mean, action_std)
         dist.log_prob(action).(dim=-)

Step 2: Implement Phase I - FWM optimization

Optimize FWM to generate next states that are identifiable by the IDM.

# Phase I: Forward World Model optimization
def phase_i_optimize_fwm(
    fwm, idm, state_transitions,
    optimizer_fwm, num_steps=100
):
    """
    Phase I: Optimize FWM to maximize conditional MI I(Z; Ŷ | X).

    Args:
        fwm: Forward World Model
        idm: Inverse Dynamics Model (frozen)
        state_transitions: Tensor of shape (batch_size, 2, state_dim)
                          containing [state, next_state] pairs
        optimizer_fwm: Optimizer for FWM
        num_steps: Number of optimization steps
    """
    for step in range(num_steps):
        # Sample random actions (latent variables)
        batch_size = state_transitions.shape[0]
        action_dim = 4  # Assuming 4D action space
        sampled_actions = torch.randn(batch_size, action_dim)

        state = state_transitions[:, 0, :]
        next_state_real = state_transitions[:, 1, :]

        # Forward model predicts next state
        next_state_pred = fwm(state, sampled_actions)

        # Inverse model evaluates identifiability
        # High log_prob from IDM = good prediction
        idm_log_prob = idm.log_prob(sampled_actions, state, next_state_pred)

        # Loss: maximize identifiability (negative for gradient descent)
        loss = -idm_log_prob.mean()

        # Backward pass
        optimizer_fwm.zero_grad()
        loss.backward()
        optimizer_fwm.step()

        if step % 20 == 0:
            print(f"  Phase I, Step {step}: Loss={loss.item():.4f}")

    return fwm

Step 3: Implement Phase II - IDM optimization

Optimize IDM to infer actions that maximize the likelihood of observed transitions.

# Phase II: Inverse Dynamics Model optimization
def phase_ii_optimize_idm(
    fwm, idm, state_transitions,
    optimizer_idm, num_steps=100
):
    """
    Phase II: Optimize IDM to maximize ELBO of log p(a | s, s').

    Args:
        fwm: Forward World Model (frozen)
        idm: Inverse Dynamics Model
        state_transitions: Tensor of shape (batch_size, 2, state_dim)
        optimizer_idm: Optimizer for IDM
        num_steps: Number of optimization steps
    """
    for step in range(num_steps):
        state = state_transitions[:, 0, :]
        next_state_real = state_transitions[:, 1, :]

        # Sample actions from IDM posterior
        action_mean, action_log_std = idm(state, next_state_real)
        action_std = torch.exp(action_log_std)
        dist = torch.distributions.Normal(action_mean, action_std)
        sampled_actions = dist.rsample()

        # Forward model predicts next state from sampled action
        next_state_pred = fwm(state, sampled_actions)

        # Reconstruction loss: how well does the action reconstruct the transition?
        recon_loss = torch.nn.functional.mse_loss(next_state_pred, next_state_real)

        # KL regularization: encourage IDM to stay close to prior (standard normal)
        kl_loss = torch.distributions.kl_divergence(
            dist, torch.distributions.Normal(torch.zeros_like(action_mean), torch.ones_like(action_std))
        ).sum(dim=-1).mean()

        # ELBO = reconstruction + KL
        loss = recon_loss + 0.01 * kl_loss

        # Backward pass
        optimizer_idm.zero_grad()
        loss.backward()
        optimizer_idm.step()

        if step % 20 == 0:
            print(f"  Phase II, Step {step}: Loss={loss.item():.4f}, "
                  f"Recon={recon_loss.item():f}, KL=")

     idm

Step 4: Implement reciprocal optimization loop

Alternate between phases until convergence.

# Reciprocal optimization
def train_swirl(
    fwm, idm, state_transitions,
    optimizer_fwm, optimizer_idm,
    num_iterations=10, steps_per_phase=50,
    device='cuda'
):
    """
    Main SWIRL training loop: alternate between Phase I and Phase II.
    """
    state_transitions = state_transitions.to(device)
    fwm = fwm.to(device)
    idm = idm.to(device)

    for iteration in range(num_iterations):
        print(f"Iteration {iteration + 1}")

        # Phase I: Optimize FWM
        print("  Phase I: Optimizing FWM...")
        fwm = phase_i_optimize_fwm(
            fwm, idm, state_transitions,
            optimizer_fwm, num_steps=steps_per_phase
        )

        # Phase II: Optimize IDM
        print("  Phase II: Optimizing IDM...")
        idm = phase_ii_optimize_idm(
            fwm, idm, state_transitions,
            optimizer_idm, num_steps=steps_per_phase
        )

        # Evaluate on test set
        test_loss = evaluate_world_models(fwm, idm, state_transitions)
        print(f"  Test loss: {test_loss:.4f}\n")

    return fwm, idm

Step 5: Integrate with GRPO for end-to-end learning

Use RL optimization for both models with self-generated rewards.

# GRPO-based SWIRL training
class SWIRLWithGRPO:
    def __init__(self, fwm, idm, verifier):
        self.fwm = fwm
        self.idm = idm
        self.verifier = verifier

    def compute_world_model_reward(self, state, next_state_pred, next_state_real):
        """
        Compute reward based on prediction accuracy.
        Could also use downstream task performance.
        """
        prediction_error = torch.norm(next_state_pred - next_state_real, dim=-1)
        reward = 1.0 / (1.0 + prediction_error)
        return reward

    def grpo_step_fwm(self, states, actions, next_states, optimizer):
        """
        GRPO step for FWM optimization.
        """
        # Generate predictions
        next_state_preds = self.fwm(states, actions)

        # Compute rewards
        rewards = self.compute_world_model_reward(
            states, next_state_preds, next_states
        )

        # GRPO loss (standard advantage estimation)
        advantages = (rewards - rewards.mean()) / (rewards.std() + 1e-8)
        loss = -(advantages * torch.log_softmax(next_state_preds, dim=-1).mean(dim=-1)).mean()

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        return loss.item()

    def grpo_step_idm(self, states, next_states, optimizer):
        """
        GRPO step for IDM optimization.
        """
        # Generate action predictions
        action_mean, action_log_std = .idm(states, next_states)

        
        action_std = torch.exp(action_log_std)
        dist = torch.distributions.Normal(action_mean, action_std)
        log_prob = dist.log_prob(torch.zeros_like(action_mean)).(dim=-)

        
        advantages = (log_prob - log_prob.mean()) / (log_prob.std() + )
        loss = -advantages.mean()

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

         loss.item()

Practical Guidance

When to use: VLM/LLM-based world modeling tasks where action-labeled data is unavailable or expensive. Works for video understanding, embodied reasoning, environment simulation.

Hyperparameters:

• steps_per_phase: 50-100 (balance between convergence and computational cost)
• num_iterations: 5-20 (typically converges in 10-15)
• KL weight in Phase II: 0.01-0.1 (controls action prior strength)
• Action dimension: Task-dependent (4-8 typical)

Key empirical findings:

• Improvements across 6 benchmarks: Aurora-Bench (+16%), ByteMorph (+28%), WorldPredictionBench (+16%), StableToolBench (+14%)
• Works without action labels; reduces annotation burden
• Self-improvement continues for 5-10 iterations
• Benefits scale with data quality, not quantity

Common pitfalls:

• Skipping initialization with supervised fine-tuning → slow convergence
• Using highly stochastic actions → IDM becomes ill-posed
• Imbalanced optimization (one model converging much faster) → divergence
• Not validating on downstream tasks → may optimize for reconstruction without semantic meaning

Scaling: Linear with state/action dimensions. Tested on 3B-7B models; scaling to 70B+ untested.

Reference

Paper: https://arxiv.org/abs/2602.06130 Code: Promised to be released Related work: World models, inverse models, latent variable models Benchmarks: Aurora-Bench, ByteMorph, WorldPredictionBench, StableToolBench