Francois Chollet 公布 GPT-6 Astra 在 ARC-AGI-3 的评测结果,使用标准 harness 得分 66%,改用连续对话 harness 加自定义 compaction 后接近 100%,每局成本约 360 美元,且连续对话版本在几乎所有关卡的动作效率上超过人类基线。
GPT-6 Astra represents a step-function change in model capability for interactive reasoning problems. It scores 66% on ARC-AGI-3 using our standard harness, and nearly 100% with a continuous conversation harness and custom compaction, at a cost of roughly $360 per game.
In fact, the continuous harness version significantly outperforms our human baseline in action efficiency across almost all levels. When we examined the reasoning chains to understand how the model operates, we found it performing highly efficient, on-the-fly symbolic world modeling for each game and level. It goes as far as developing its own shorthand DSL to represent in-game situations -- essentially a game-specific algebraic notation.
Overall, Astra exhibits symbolic modeling behaviors we had previously only seen with sophisticated harnesses -- so harness capabilities are increasingly shifting into the model itself.
We see Astra as a major breakthrough in model intelligence.
Read our post on Astra and what these results mean: https://t.co/wJnYxEqYNI
来源:@fchollet · x.com