[Misc] Add exponential distribution to multi-turn benchmark

[nikonyrh-siloai]

Purpose

The multi-turn benchmark is missing exponential distribution, which is common on many contexts.

Test Plan

Add the implementation and test sampling.

Test Result

import bench_dataset
dist = bench_dataset.get_random_distribution({"prompt_input": {"num_turns": {"distribution": "exponential", "scale": 10, "max": 15}}}, "prompt_input", "num_turns")
dist.sample(10)
# array([11.66666324, 15.        ,  7.12551583,  8.96139177, 10.77674281,
#         7.64838007,  9.02449013,  1.75445604,  4.76282046,  3.71192744])

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

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[gemini-code-assist]

Code Review

This pull request introduces the ExponentialDistribution class to the multi-turn benchmark dataset, enabling exponential sampling for conversation parameters, and updates the documentation accordingly. Review feedback identifies a critical issue where the sample method returns floats, which would cause TypeErrors in downstream code expecting integers; a fix to round and cast these samples was suggested. Additionally, the feedback recommends adding input validation for the scale parameter and improving type hints for better robustness.