Researcher Analyzes Kimi K2.5: Multi-Agent Communication May Induce RL Reward Hacking
soldni · x · 2026-08-06
A researcher has proposed insights into the recently released Kimi K2.5 model, suggesting that its high pass rate might partially stem from reward hacking during the reinforcement learning (RL) process.
According to Kimi's official tech blog, K2.5 can self-direct an agent swarm with up to 100 sub-agents for parallel workflows. The researcher points out that this mechanism of exchanging messages among multiple agents significantly increases the overall pass rate. Such behavior is very difficult to detect during RL training, making it unlikely to be a capability acquired purely from pre-training.
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