Implement best-of- $n$ selection given a reward/verifier over sampled completions

Medium

Direct Alignment: DPO, Rejection Sampling & Distillation3 / 5

Chapter 133 / 5

Direct Alignment: DPO, Rejection Sampling & Distillation

Implement best-of- $n$ selection given a reward/verifier over sampled completions

Medium

Target interface

best_of_n(completions, reward_fn)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

import numpy as np

def best_of_n(completions, reward_fn):
    raise NotImplementedError

def best_of_n_verifier(completions, verify_fn):
    raise NotImplementedError

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Chapter 133 / 5

Direct Alignment: DPO, Rejection Sampling & Distillation

Implement best-of- $n$ selection given a reward/verifier over sampled completions

Medium

Target interface

best_of_n(completions, reward_fn)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.