Decision Retention Is Not Trajectory Retention: Preserving Cross-Hero Capabilities During Offline-to-Online Game Reinforcement Learning

Offline imitation can produce behavior that transfers beyond the demonstrated task, while narrow online reinforcement learning may erase those untrained capabilities. We study this problem in the licensed Honor of Kings Gamecore using a corpus of 1.78 million decisions from one controlled hero. Across six training seeds and 1,422 Gamecore matches, twenty source-only PPO updates reduce cross-hero scheduled win rate from 38.9% to 29.2% (four of six seeds negative) without a reliable source-reward gain. Source-policy distillation restores cross-hero reward and win rate to near their pre-adaptation levels. We introduce capability-support distillation (CSD), which stores reward-free states and legal-action constraints from sentinel heroes and distills the frozen pre-adaptation policy during online learning. On 39,094 sentinel states, CSD raises teacher agreement from 75.78% under source distillation to 79.35%. In a one-time held-out evaluation released by a hashed decision file fixed before any held-out CSD game, CSD improves over unconstrained PPO by 14.59 reward points [3.21, 26.47] and 20.83 percentage points [8.33, 33.33]; a second independent held-out run shows the same direction with smaller gaps, and pooled over both runs the advantage is 10.63 reward points [2.68, 19.82] and 13.2 percentage points [5.6, 21.5]. Pooled, CSD matches but does not exceed the pre-adaptation policy, and its reward differences from behavioral cloning, hard replay, and source distillation include zero. On sentinel heroes, where the five methods win one of 270 games, CSD agrees with the teacher most closely yet loses fewer games and reaches the step cap more often than the teacher. Decision retention on stored states is therefore measurable but is not by itself evidence of trajectory retention.