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How training works

The Training section showing overall stats, study plans with Decision and Recall modes, and recent session history
Training in RangeSharp tests your knowledge of the ranges you’ve built or imported. You practice making decisions on random hands, and RangeSharp tracks your accuracy, identifies patterns in your mistakes, and schedules reviews using spaced repetition.

Two training modes

ModeWhat you doWhat it tests
DecisionSee a hand, choose an actionReal-time decision-making speed
RecallReproduce a full range from memoryDeep knowledge of the complete range
Most players should focus on Decision mode for daily practice and use Recall mode to verify mastery.

Two ways to start training

Quick train

Press ⌘T (or click the Train button in the toolbar) while viewing any spot in the Study workspace. This immediately starts a Decision mode session on that single spot.

Study plan

Open the Train section from the nav rail, select a study plan, and click Start. This runs a session across all the spots in the plan, with spaced repetition determining which spots appear first.

During a session

Every training session includes:
  1. Hand presentation — You see a poker table with your position, the action context, and your hole cards
  2. Your answer — Pick the correct action or paint the range
  3. Feedback — Immediate right/wrong feedback with the correct answer shown
  4. Progress tracking — Accuracy percentage and streak count update in real time

After a session

When you end a session (press Esc or complete the plan), RangeSharp shows:
  • Session summary — Accuracy, hands played, EV lost, longest streak
  • Mistake list — Every hand you got wrong, with the correct action
  • Review option — Step through your mistakes one by one to study them
The session data feeds into your spaced repetition cards and mastery levels.

Training features at a glance

Training limits by plan

FeatureFreeProElite
Training hands/day100UnlimitedUnlimited
Training modesBothBothBoth
RNG for mixed strategiesNoYesYes
AI debriefs/day11030