On the Learning Mechanisms in Physical Reasoning

Abstract: Is dynamics prediction indispensable for physical reasoning? If so, what kind of roles do the dynamics prediction modules play during the physical reasoning process? Most studies focus on designing dynamics prediction networks and treating physical reasoning as a downstream task without investigating the questions above, taking for granted that the designed dynamics prediction would undoubtedly help the reasoning process. In this work, we take a closer look at this assumption, exploring this fundamental hypothesis by comparing two learning mechanisms: Learning from Dynamics (LfD) and Learning from Intuition (LfI). In the first experiment, we directly examine and compare these two mechanisms. Results show a surprising finding: Simple LfI is better than or on par with state-of-the-art LfD. This observation leads to the second experiment with Ground-truth Dynamics, the ideal case of LfD wherein dynamics are obtained directly from a simulator. Results show that dynamics, if directly given instead of approximated, would achieve much higher performance than LfI alone on physical reasoning; this essentially serves as the performance upper bound. Yet practically, LfD mechanism can only predict Approximate Dynamics using dynamics learning modules that mimic the physical laws, making the following downstream physical reasoning modules degenerate into the LfI paradigm; see the third experiment. We note that this issue is hard to mitigate, as dynamics prediction errors inevitably accumulate in the long horizon. Finally, in the fourth experiment, we note that LfI, the extremely simpler strategy when done right, is more effective in learning to solve physical reasoning problems. Taken together, the results on the challenging benchmark of PHYRE show that LfI is, if not better, as good as LfD for dynamics prediction. However, the potential improvement from LfD, though challenging, remains lucrative.

• The paper reveals that simple LfI models, such as ViT, can outperform complex LfD architectures in cross-template settings through better generalization.
• It demonstrates that while ground-truth dynamics boost performance, approximate predictions often degrade outcomes by mimicking intuitive behavior.
• Optimization results indicate that parallel training schedules struggle with compounded errors over long prediction horizons, underscoring the efficacy of LfI approaches.

On the Learning Mechanisms in Physical Reasoning

The paper "On the Learning Mechanisms in Physical Reasoning" explores the roles of dynamics prediction in physical reasoning tasks. It examines whether dynamics prediction is indispensable or merely assumed as helpful, comparing Learning from Dynamics (LfD) and Learning from Intuition (LfI) mechanisms.

Learning Mechanisms

Learning from Intuition (LfI)

LfI involves directly modeling the outcome from the initial state and action without explicitly simulating future dynamics. The architecture consists merely of a task-solution model, formulated as $P(y | X_0) = f(X_0; \theta)$, where $X_0$ encapsulates the initial conditions.

Learning from Dynamics (LfD)

LfD theoretically predicts object dynamics over time and uses these predictions for task-solving. It involves a dynamics predictor $g(\cdot)$ and a subsequent decision-making model $f(\cdot)$. The process is defined as $P(y | X_0) = f(D; \theta)$, where $D = g(X_0; \phi)$.

To handle the dynamics prediction, the paper describes parallel and serial optimization methods where either the dynamics predictor and the solution model are trained jointly or sequentially.

Experimental Setup

Experiments employed the PHYRE-B benchmark, a goal-driven test suite of physical puzzles. The paper conducted four major experiments:

1. Comparing state-of-the-art LfD models with LfI models.
2. Testing LfD under Ground-truth Dynamics (GD) to establish upper bounds.
3. Evaluating LfD with Approximate Dynamics (AD) predictions.
4. Exploring the performance of various LfI models.

AUCCESS was utilized as the performance metric, rewarding fewer attempts to solve puzzles based on weighted success rates.

Key Findings

LfI vs. LfD

Contrary to prevailing assumptions, the experiment reveals that simple LfI models like ViT can outperform complex LfD models in cross-template settings due to better generalization capabilities. This suggests that dynamics prediction modules might not be essential.

Dynamics Prediction Impact

While ground-truth dynamics significantly improve problem-solving effectiveness (increased AUCCESS), real-world dynamics prediction comparatively underperforms, resulting in models degenerating to LfI-like capabilities.

Optimization Schedules

The disparity between parallel and serial optimization schedules manifests due to the compounding errors over long prediction horizons in approximating dynamics. Parallel optimization struggles to reconcile prediction inaccuracies with task outcomes effectively.

Simplified LfI Models

Various LfI architectures, including ViT, Swin Transformer, and BEiT, showcase competitive performance with minimal design complexity. These models inherently leverage spatial structure without explicit dynamics, favoring across-template generalization.

Conclusion

Dynamics prediction can theoretically aid physical reasoning but practical predictions often degrade performance due to compounded inaccuracies. LfI stands out as a simpler yet effective paradigm for many reasoning tasks across different scenarios, driving a potential paradigm shift from complex dynamics-based models to intuitive models.

Future Directions

Potential areas for further exploration include:

• More comprehensive environments beyond PHYRE-B to generalize findings.
• Fine-tuning LfI model architectures with explicit inductive biases for physics reasoning.
• Investigation into the precise level of accuracy necessary for beneficial dynamics in real applications.
• Continual pursuit of feasible routes in dynamics predictions for speculative reasoning tasks like counterfactuals.

This paper sheds light on the fundamental assumptions of dynamics prediction in AI and motivates reconsideration of intuition-based models as a primary strategy in solving complex physical reasoning tasks.