Unverified50% confidenceFactExact time
The quality of reward signals in RLVR depends on the task difficulty distribution: tasks that are too easy lead to reward saturation and vanishing gradients, while tasks that are too hard lead to sparse rewards, both of which cause training to stall.
1
Sources
50%
Confidence
Long-term
Relevance
10/9/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen the task success rate approaches zero, RLVR fails due to the lack of successful samples and is unable to provide effective optimization gradient signals.79% similarUnverifiedIn reward function design, sparse rewards can easily cause training to stagnate, and introducing dense reward signals such as distance reduction can accelerate convergence.77% similarUnverified低精度引入的数值扰动会在RL的多轮迭代中通过策略梯度的乘积形式被放大,可能导致训练崩溃或模型性能退化72% similarUnverifiedNumerical inconsistency between the trainer and inference engine (train-inference mismatch) is a common hidden cause of RL training instability and policy degradation.72% similarVerifiedDrift detection involves a trade-off: setting the threshold too sensitively leads to frequent false alarms and unnecessary retraining, while setting it too loosely may allow model performance to continuously degrade without being detected.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/990695API
curl https://kongchang.com/api/v1/knowledge/claims/990695MCP
get_claim(id=990695)