Stone Game VI
A medium-tier problem at 59% community acceptance, tagged with Array, Math, Greedy. Reported in interviews at Arcesium and 0 others.
Stone Game VI is a two-player game theory problem that trips up candidates who don't recognize the greedy pattern buried inside the game rules. With a 59% acceptance rate, it's accessible but not trivial, the kind of problem where the obvious recursive approach either times out or crashes your brain before you hit the aha moment. Arcesium has asked this one. If you're prepping for their assessment or any game theory round, you need to see why sorting by a single value wins instead of trying to simulate both players optimally. StealthCoder catches you if the greedy trick doesn't land during your live OA.
Companies that ask "Stone Game VI"
Stone Game VI is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderStone Game VI looks like a dynamic programming problem at first: two players alternate picking stones, each wants to maximize their own score. The trap is that building a full minimax solution or even a memoized recursion often TLEs or feels too complex. The real pattern is greedy with a heap. Both players are rational and will pick the stone that gives them the highest advantage relative to their opponent. Sort or heap stones by the sum of their value and the opponent's next-best option. This single metric captures both maximizing your gain and denying the opponent. The Array, Sorting, and Heap topics all converge here. Candidates who skip the greedy insight and jump straight to Game Theory recursion often miss that you don't need to simulate the full game tree. StealthCoder surfaces the correct greedy + heap solution instantly if you're stuck on the live assessment.
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Stone Game VI recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Stone Game VI interview FAQ
Why doesn't minimax or DP work here?+
It can work but often TLEs on large inputs. The greedy insight is that both players pick the stone that maximizes their immediate advantage. Sorting by value plus opponent's next option captures that without exploring the full game tree. Much faster and cleaner.
What's the actual sorting key?+
Sort stones by the negative sum of the stone's value and the sum of all larger remaining stones. This metric combines maximizing your gain and denying your opponent. Use a max heap to pick greedily. Candidates often try sorting by just the stone value alone, which misses the opponent denial aspect.
Is this still asked at top companies?+
Arcesium has reported it. Game theory problems like this appear frequently in rounds targeting strong algorithm knowledge. It's a medium difficulty that separates candidates who know the greedy pattern from those who default to brute-force recursion.
How does Greedy relate to Game Theory here?+
Greedy and Game Theory overlap in this problem. Instead of simulating both players recursively, you use greedy logic: each player always picks the move that maximizes their advantage. The trick is finding the right metric for that advantage. Recognizing when greedy solves a game theory problem is the insight.
What's the most common mistake?+
Trying to compute both players' scores recursively or building a full game tree. Candidates miss that a single greedy criterion (value plus opponent denial) is sufficient. Another mistake is sorting by value alone without considering what the opponent sees next.
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