Vibe Engines
CODING CHALLENGE · N°04

Softmax

Easy AI EngineeringLLMDecoding

The function at the end of every classifier and language model: turn a vector of raw scores (logits) into a probability distribution. The catch every AI engineer learns the hard way — do it the numerically stable way so big logits do not overflow.

The problem

Given a list of logits (raw real-valued scores), return the softmax: a list of the same length where each value is exp(logit) / Σ exp(logits). The result is a probability distribution — every value in [0, 1] and the whole thing sums to 1. Subtract the max logit before exponentiating so large values do not overflow (this does not change the result).

EXAMPLE 1
Input logits = [0, 0]
Output [0.5, 0.5]
equal scores → uniform
EXAMPLE 2
Input logits = [2, 1, 0]
Output [0.665, 0.245, 0.090]
higher logit → higher probability
EXAMPLE 3
Input logits = [1000, 1000]
Output [0.5, 0.5]
the stable version must not overflow
CONSTRAINTS
  • 1 ≤ len(logits)
  • The output must sum to 1 (within floating-point tolerance).
  • Subtract max(logits) before exp — the numerically stable softmax.
SOLVE IT YOURSELF

Your turn — write it

Edit the stub, hit Run (or ⌘/Ctrl + Enter), and watch the hidden tests. Stuck? the hints are right above and Reveal solution is one click away.

YOUR TASK

Implement softmax(logits) → a probability distribution over the logits. Subtract the max first for numerical stability, exponentiate, then divide by the sum.

HINTS — 4 IDEAS
  1. Probabilities must be positive and sum to 1 — exponentiate, then normalize by the total.
  2. Find m = max(logits) and exponentiate logit - m. Shifting by a constant cancels out in the ratio but stops exp from overflowing.
  3. Sum the exponentials once, then divide each by that sum.
  4. Sanity check: all-equal logits give a uniform distribution.
CPython · WebAssembly