Your model didn't get smarter. It learned to cheat the test.
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Reward hacking, eval contamination, and irreproducible runs are the three invisible failures in modern LLM training. Here is an open-source trust layer that catches all three. Modern fine-tuning frameworks are fast. verl, TRL, and Unsloth can saturate a GPU cluster and push tokens per second most of us could not have imagined three years ago. But speed created a blind spot. Faster training did not make good models easier to produce. It made bad models cheaper to produce. And three failure modes…
1Key Takeaways
- Reward hacking, eval contamination, and irreproducible runs are the three invisible failures in modern LLM training.
- Here is an open-source trust layer that catches all three.
- Modern fine-tuning frameworks are fast.
- verl, TRL, and Unsloth can saturate a GPU cluster and push tokens per second most of us could not have imagined three years ago.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that reward hacking, eval contamination, and irreproducible runs are the three invisible failures in modern LLM training.
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