specific method · filed under inference & serving
Parallel-fewest-step sampling
The method samples independent trajectories in parallel and selects the one with the fewest steps.
Also called parallel scaling, Parallel-fewest-step.
- sources
- 2
- model
- 1
- labs adopt it
- 0
- strongest
- evaluated
How sources treat it
One count per evidence span, weakest treatment to strongest.
evaluated 2
Documented in
Evidence
2 spans quoted from the sources, strongest treatment first.
Parallel-fewest-step, which samples N independent trajectories and selects the trajectory with the fewest steps.
evaluatedinference servingin DeepSeek-V3.2DeepSeek
Parallel-fewest-step, which samples N independent trajectories and selects the trajectory with the fewest steps.
evaluatedinference servingin DeepSeek-V3.2DeepSeek
Filed alongside
Other methods under inference & serving :: reasoning control.
Configurable reasoning effortChain-of-thought reasoningDefault thinking modeDeployment-time scalar effort controlDisabling reasoning via chat-template configurationInference-time reasoning budget controlInterleaved thinking between tool callsThinking mode selectionCross-turn persistent reasoning historyMaximum thinking effortReasoning parserAlways-on thinking modeclear_thinking chat-template parameterControl-token-enabled thinking modeEffort-dependent exponential token-penalty scheduleGenerate-verify-refine loopMedium-effort reasoning modeQwen3 soft thinking switchTask- and mode-specific sampling parameter recommendationsTest-time compute scalingAdaptive reasoningCapped linear reasoning-token length deductionConfigurable thinking or reasoning modeControllable thinking effort via system message and per-token cost