Unverified50% confidenceFactExact time
In RL training, performance variance across random seeds can be extremely large, hyperparameters have a far stronger effect on results than in supervised learning, and debugging cycles are long with high compute consumption.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
Related Entities
Related Claims
Unverified强化学习训练比监督学习更不稳定,将NVFP4激进量化引入RL训练会进一步放大固有不稳定性78% similarUnverifiedThe optimization landscape of RLVR is fundamentally different from supervised learning: the reward signal is sparse and discrete, gradient estimates come from sampled responses, and the variance is significantly higher than in traditional training settings.77% similarUnverifiedHigh-leverage points significantly inflate the training-set R² while contributing little to out-of-sample performance, thereby causing traditional shrinkage formulas to overestimate predictive ability.76% similarUnverifiedThe RL training process involves the exploration-exploitation tradeoff, the high variance of policy gradient estimation, and the risk of reward hacking caused by improperly designed reward functions.76% similarUnverifiedIn reinforcement learning training for LLMs, when a model repeatedly fails on high-difficulty problems, standard RL algorithms tend to bypass these problems and instead reinforce on simpler tasks where rewards are easier to obtain.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1117332API
curl https://kongchang.com/api/v1/knowledge/claims/1117332MCP
get_claim(id=1117332)