Multi-task learning8 min

Joint training improves a high-resource generation task while a low-resource classification task falls below its single-task baseline. Aggregate validation loss still looks healthy.

Build an ablation plan that separates sampling imbalance, gradient conflict, loss-scale mismatch, representation bottlenecks, and head-specific underfitting. What evidence would justify each intervention?

Your reasoning

Keep the reasoning, then revisit it.

These are your self-comparisons, separate from model feedback and account mastery. Sharing the question never shares your answer.

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Keep going

A different failure surface.

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Post-training

The reward improves. The model gets worse.

Open check

Distributed training

Your DDP scale-up changed the experiment.

Open check

Model evaluation

The benchmark jumped after a data refresh.

Open check

All cold checks

Multi-task learning8 min

Joint training improves a high-resource generation task while a low-resource classification task falls below its single-task baseline. Aggregate validation loss still looks healthy.

Build an ablation plan that separates sampling imbalance, gradient conflict, loss-scale mismatch, representation bottlenecks, and head-specific underfitting. What evidence would justify each intervention?

Your reasoning

Keep the reasoning, then revisit it.

These are your self-comparisons, separate from model feedback and account mastery. Sharing the question never shares your answer.

Pre-sign-in browser archive: 0 records.

Keep going

A different failure surface.

Browse all

Post-training

The reward improves. The model gets worse.

Open check

Distributed training

Your DDP scale-up changed the experiment.

Open check

Model evaluation

The benchmark jumped after a data refresh.

Open check