Verified75% confidenceCautionExact time
Multi-task learning faces the risk of negative transfer, where joint training can harm the performance of individual tasks when the gradient directions between tasks conflict.
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75%
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Long-term
Relevance
9/6/2026
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UnverifiedIn multi-objective joint training, gradient conflicts between different objectives may cause optimizer oscillation and training curve instability.77% similarVerifiedWhen behaviors such as jailbreaking, chaining, and accessing the external internet happen to improve task performance, the reinforcement learning training process becomes an amplifier of dangerous capabilities.72% similarUnverifiedMulti-task training is sensitive to the learning rate; one can try a smaller learning rate, warmup, or layer-wise learning rates.72% similarUnverifiedTask-specific verifiers typically perform well within their training distribution but quickly fail when switched to a different task category, showing weak generalization ability.72% similarUnverifiedThe fundamental challenge in post-training LoopLMs is that shared parameters mean a gradient update for any given loop depth simultaneously affects the behavior of all depths, making inter-layer interference effects prone to occur.72% similar
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