Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is generated by scrambling simple expressions and recording the inverse operations, creating oracle trajectories that provide both goal states and explicit paths to reach them. A permutation-equivariant, transformer-based policy network is then trained on this data step-wise to predict the oracle action given the input expression. We demonstrate this approach on two problems in high-energy physics: dilogarithm reduction and spinor-helicity scattering amplitude simplification. In both cases, our trained policy network achieves near perfect solve rates ac...
| Authors | David Shih |
|---|---|
| Primary category | hep-th |
| Published | 11 March 2026 |
| Last updated at source | 11 April 2026 |
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