Unverified50% confidenceSolutionExact time
The truly effective approach is per-bin attribution of the calibration penalty; only by creating reward differences across different confidence intervals can a meaningful gradient signal be produced.
1
Sources
50%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTRACER designs hierarchical rewards that provide reward signals at both the outcome level and the trajectory level, combined with bias-aware advantage modulation to mitigate reward sparsity and credit assignment problems.73% similarUnverifiedIf a benchmark rewards "giving an answer" without penalizing "wrong answers," guessing is statistically a worthwhile strategy.71% similarUnverifiedThe greatest risk of self-improvement comes from the reward signal itself: if the reward is improperly defined, the agent will optimize evaluation metrics rather than the true objective (the reinforcement learning version of Goodhart's Law).69% similarUnverifiedCommon practices for mitigating reward hacking include introducing penalty terms, using multi-objective reward weighting, or incorporating scheduling fairness constraints into the reward.69% similarUnverified主流反爬服务商普遍采用风险评分机制而非简单二元判断,分数超过阈值才触发拦截68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/956118API
curl https://kongchang.com/api/v1/knowledge/claims/956118MCP
get_claim(id=956118)