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๐ญ Do thinking traces make Language Models learn better? Curious what others think
๐ฆ๐ฐ๐ฒ๐ป๐ฎ๐ฟ๐ถ๐ผ
You take an instruction-following LM.
You want to train it with a GRPO-style RL algorithm on a task like Tic Tac Toe.
Rewards are outcome-based, applied only at the end of each episode: win/loss/draw, format adherence...
During training, the model could just output answers, but a common choice is to make it also output thinking traces.
๐ง๐ต๐ฒ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป
Does forcing the model to produce thinking traces during training actually improve learningโ
๐ฌ I'd like to hear your thoughts. Share ideas and links to relevant papers and resources.
From what I've understood so far, the answer seems to be ๐๐ฒ๐.
1๏ธโฃ If you force the model to think during training, it becomes a model that thinks at inference time. It naturally allocates more budget (tokens) to a problem, which tends to improve performance.
2๏ธโฃ While the model's "reasoning" already exists in its activation space, using explicit thinking traces as a scratchpad allows training to steer and shape that reasoning.
3๏ธโฃ As the model produces more traces during training, the RL algorithm can progressively give higher rewards to the reasoning patterns that lead to better outcomes.
๐ฆ๐ฐ๐ฒ๐ป๐ฎ๐ฟ๐ถ๐ผ
You take an instruction-following LM.
You want to train it with a GRPO-style RL algorithm on a task like Tic Tac Toe.
Rewards are outcome-based, applied only at the end of each episode: win/loss/draw, format adherence...
During training, the model could just output answers, but a common choice is to make it also output thinking traces.
๐ง๐ต๐ฒ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป
Does forcing the model to produce thinking traces during training actually improve learningโ
๐ฌ I'd like to hear your thoughts. Share ideas and links to relevant papers and resources.
From what I've understood so far, the answer seems to be ๐๐ฒ๐.
1๏ธโฃ If you force the model to think during training, it becomes a model that thinks at inference time. It naturally allocates more budget (tokens) to a problem, which tends to improve performance.
2๏ธโฃ While the model's "reasoning" already exists in its activation space, using explicit thinking traces as a scratchpad allows training to steer and shape that reasoning.
3๏ธโฃ As the model produces more traces during training, the RL algorithm can progressively give higher rewards to the reasoning patterns that lead to better outcomes.