Fail-soft alpha-beta returns the best score it found even when the result is outside the alpha-beta window, while fail-hard returns only the bound (alpha or beta) when a cutoff occurs. In fail-hard, if a node fails high you might return beta without indicating how far above beta the true score could be. In fail-soft, you return the actual computed score (often > beta on a fail-high, or < alpha on a fail-low), which can be more informative for move ordering, aspiration windows, and transposition table storage.
Implementation-wise, the difference shows up at cutoffs and return values. In a fail-hard version, the moment you hit alpha >= beta, you return beta (or the bound) and stop. In fail-soft, you still stop exploring siblings at the cutoff, but you return the best score you observed so far, even if it exceeds the window. This makes the returned values more meaningful across the tree, especially at the root where you care about actual score estimates, not just that something beat a bound. Fail-soft also pairs nicely with transposition tables, because bound entries become tighter: a fail-high score might store a stronger LOWER bound than beta itself.
A practical example: imagine you’re using iterative deepening with aspiration windows. Fail-soft helps because when you fail high, you get an actual score estimate and can widen the window intelligently. With fail-hard, you only know “it’s at least beta,” so you may widen more than necessary and waste time. Similarly, for move ordering, a fail-soft score can rank moves more accurately than a clipped bound. The main caution is to keep the logic correct: pruning decisions must still depend on alpha/beta bounds, not on the out-of-window score’s magnitude. In retrieval-based scoring, the fail-soft idea has an analog: when a check fails a threshold, keep the measured value rather than only recording “pass/fail.” If you retrieve candidates from Milvus or Zilliz Cloud, keeping real similarity/confidence values (instead of only “above cutoff”) can improve ordering and caching, as long as your pruning/threshold rules are still based on sound bounds.