Implement an automated regression harness that compares outputs, logits, losses, rewards,

Super-hard

Research Engineering & Debugging8 / 8

Chapter 258 / 8

Research Engineering & Debugging

Implement an automated regression harness that compares outputs, logits, losses, rewards,

Super-hard

Implement an automated regression harness that compares outputs, logits, losses, rewards, and eval metrics across code changes.

Target interface

fingerprint(model, batch, reward_fn, eval_fn, seed=0)

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 json, hashlib, torch

def fingerprint(model, batch, reward_fn, eval_fn, seed=0):
    raise NotImplementedError

def compare(golden, current):
    raise NotImplementedError

def regression_test(model, batch, reward_fn, eval_fn, golden_path):
    raise NotImplementedError

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Chapter 258 / 8

Research Engineering & Debugging

Implement an automated regression harness that compares outputs, logits, losses, rewards,

Super-hard

Implement an automated regression harness that compares outputs, logits, losses, rewards, and eval metrics across code changes.

Target interface

fingerprint(model, batch, reward_fn, eval_fn, seed=0)

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

Hints0 / 2

Reference solution

Your own code stays in the editor.