In-Context Reinforcement Learning for Variable Action Spaces — part 4/4

Cleaned-text code points [47461, 48911). Bibliography omitted here; source.md includes it.

We illustrate how the probability mass can unintentionally be put on unwanted actions through an analysis of cosine similarities between action embeddings, comparing orthonormal vectors to those from a standard normal distribution. Figure 14 demonstrates non-zero cosine similarities for the standard normal distribution, indicating that a vector perfectly aligned with one action embedding may erroneously attribute non-zero probabilities to other actions.

## Appendix L Code Sample

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