Training and Agentic Inference Strategies for LLM-based Manim Animation Generation
Generating programmatic animation using libraries such as Manim presents unique challenges for Large Language Models (LLMs), requiring spatial reasoning, temporal sequencing, and familiarity with domain-specific APIs that are underrepresented in general pre-training data. A systematic study of how training and inference strategies interact in this setting is lacking in current research. This study introduces ManimTrainer, a training pipeline that combines Supervised Fine-tuning (SFT) with Reinforcement Learning (RL) based Group Relative Policy Optimisation (GRPO) using a unified reward signal that fuses code and visual assessment signals, and ManimAgent, an inference pipeline featuring Re...
| Authors | Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 31 August 2026 |
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