implementation detail · filed under post-training
Hierarchical Penalty Escalation
A hierarchical rejection and penalty procedure that drops contexts or sequences with no surviving turns and rejects samples with no surviving sequence.
Also called Penalties escalate along the hierarchy.
- source
- 1
- models
- 2
- lab adopt it
- 1
- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
a context with no surviving model turns is dropped, a sequence with no surviving context receives zero advantage, and a sample with no surviving sequence is rejected
usedunclearin MiMo-V2.6 RL and OPD infrastructureXiaomi
Filed alongside
Other methods under post-training :: reinforcement learning algorithm.
Group Relative Policy OptimizationReinforcement LearningGroupwise Advantage RedistributionFreezing the MoE Router During Reinforcement LearningIcePopOff-Policy Sequence MaskingReinforcement Learning from Verifiable RewardsRollout Routing ReplayUnbiased KL EstimateAsynchronous Group Relative Policy OptimizationChain-of-Thought Reinforcement LearningDirect Double-Sided Importance SamplingKeep Sampling MaskMixed Reinforcement LearningPivot Reinforcement LearningReinforcement Learning Post-TrainingAbstention TrainingAdvantage ShapingAgentic RL Task MixCISPO with Length-Weighted Leave-One-Out Group-Relative AdvantagesConcatenated Routing ReplayDerived-Latency PenaltyDomain-Specialized RL ExpertsDomain-Specific GRPO Training