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Sophie Wang

@sophielwang.bsky.social
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
📎 paper: arxiv.org/abs/2610.06851 ✍ blog: sophielwang.com/cues 💻 code: github.com/sophicle/cues
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
Very grateful for this collaboration across MIT, UC Berkeley, UW, and Ai2!
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
This project was co-led with Amil Dravid! Thank you to my amazing coauthors Rulin Shao, Kevin Farhat, Sewon Min, and Alyosha Efros. I learned so much from everyone ☺️🫶
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
Our paper follows a growing body of work questioning the extent to which base models know how to reason, and what post-training adds. We hope to deepen our greater understanding of reasoning by showing how simple token associations elicit complex behavior through training data.
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
This behavioral sensitivity to starting tokens in reasoning extends to safety. E.g., the reasoning cue “.\n\nOkay” makes the base model refuse more like its post-trained counterparts. Yet, a small formatting change to “ Okay,” makes it more compliant with harmful requests.
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
Token cues create changes in behavior that are also present in hidden-state representations. For instance, effective reasoning cues shift hidden states toward reasoning traces from the training data.
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
So, what makes these tokens special? The answer is in the data! We causally trace the effect of these token cues to associations in training data. For example, simply replacing "okay" with "chicken" in example traces in the data then makes "chicken" elicit reasoning. 🐔
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
Next, we find that much of RL’s improvement comes from upsampling token cues that already elicit reasoning in the base model. In fact, the largest policy changes occur within the first two tokens.
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
First, we discover short *token cues* that can bring base models’ pass@1 accuracy on math and code to the level of their RL-trained counterparts. We prefill “.\n\nOkay” for Olmo and “ Alright,” for Qwen, without changing any model weights.
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Sophie Wang @sophielwang.bsky.social · 06/10/2026
Can “chicken” make a base model reason? 🐔 Yes! Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too. project page: sophielwang.com/cues
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