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println(msg); // Count: 42
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To explore this, I applied MCTS across reasoning steps to Qwen-2.5-1.5B-Instruct, to search for stronger trajectories and distill these back into the model via an online PPO loop. On the task of Countdown, a combinatorial arithmetic game, the distilled model (evaluated without a search harness) achieves an asymptotic mean@16 eval score of 11.3%, compared to 8.4% for CISPO and 7.7% for best-of-N. Relative to the pre-RL instruct model (3.1%), this is an 8.2 percentage point improvement.
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