---
language: en
license: mit
tags:
  - physics
  - simulation
  - language-model
  - icml2026
---

# PhysicsLM

Anonymous submission for ICML 2026: **"PhysicsLM: Autoregressive Language Modeling of 2D Rigid Body Dynamics"**

PhysicsLM fine-tunes LFM2-350M (LiquidAI) via LoRA on 900K 2D rigid-body physics scenes,
learning to predict next simulation states as structured decimal text.

## Model details

- **Base model**: LiquidAI/LFM2-350M
- **Fine-tuning**: LoRA (r=32, alpha=64), 5-stage curriculum on PhysicsScenes
- **Task**: Next-frame physics prediction (autoregressive text generation)
- **Format**: structured decimal text encoding of 2D object states

## Results (seen scenarios)

| Category | PhysicsLM RMSE (px) | Copy-last RMSE | Linear extrap RMSE |
|----------|--------------------|-----------------|--------------------|
| Stacking | 2.60 | 6.72 | 0.06 |
| Constraint | 1.35 | 4.99 | 0.06 |
| Collision | 5.37 | 7.69 | 0.09 |
| Ramp | 18.85 | ... | 0.19 |
| Minigame | 36.14 | ... | 0.09 |
| Complex | 109.57 | ... | 0.04 |

OOD: near-distribution 0.94 px RMSE, novel OOD 24.79 px RMSE. Parse failure: 0.0%.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("anonsubmiticml2026/PhysicsLM")
model = AutoModelForCausalLM.from_pretrained("anonsubmiticml2026/PhysicsLM",
                                              torch_dtype=torch.bfloat16,
                                              device_map="cuda")
# See paper for text encoding format
```

## Dataset

Training data: [anonsubmiticml2026/PhysicsScenes](https://huggingface.co/datasets/anonsubmiticml2026/PhysicsScenes)

Code: [anonsubmiticml2026/physics-llm-paper](https://huggingface.co/anonsubmiticml2026/physics-llm-paper)