Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by
leveraging their underlying representations? We answer this question with Resa,
a family of 1.5B reasoning models trained via a novel and efficient sparse
autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to
capture reasoning abilities from a source model, and then uses the trained SAE
to guide a standard supervised fine-tuning process to elicit such abilities in
a target model, all using verified question-answer data without any reasoning
traces. Notably, when applied to certain base models before further RL
post-training, SAE-Tuning retains >97% of its RL-trained counterpart's
reasoning performance while reducing training costs by >2000x to roughly \1
and training time by >450x to around 20 minutes. Furthermore, when applied to
lightly RL-trained models (e.g., within 1 hour on 2 GPUs), it enables reasoning
performance such as 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23 for only
around 1 additional cost. Surprisingly, the reasoning abilities extracted via
SAEs are potentially both generalizable and modular. Generality means abilities
extracted from one dataset still elevate performance on a larger and
overlapping corpus. Modularity means abilities extracted from Qwen or Qwen-Math
can be attached to the R1-Distill model at test time, without any retraining,
and yield comparable gains. Extensive ablations validate these findings and all
artifacts are fully open-sourced.
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Shangshang Wang
claimed_verified
2025-06-13T07:41:31.479Z
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Shangshang Wang
user
upup-ashton-wang
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Julian Asilis
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Ömer Faruk Akgül
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2025-06-16T07:17:07.317Z
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Omer Faruk Akgul
user
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Enes Burak Bilgin
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Ollie Liu
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Deqing Fu
claimed_verified
2025-06-13T07:41:29.284Z
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Deqing Fu
user
deqing
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Willie Neiswanger
claimed_verified
2025-06-13T07:41:27.301Z
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Willie Neiswanger
user
willieneis
Resa: Transparent Reasoning Models via SAEs
How cost-effectively can we elicit strong reasoning in language models by
leveraging their underlying representations? We answer this question with Resa,
a family of 1.5B reasoning models trained via a novel and efficient sparse
autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to
capture reasoning abilities from a source model, and then uses the trained SAE
to guide a standard supervised fine-tuning process to elicit such abilities in
a target model, all using verified question-answer data without any reasoning
traces. Notably, when applied to certain base models before further RL
post-training, SAE-Tuning retains >97% of its RL-trained counterpart's
reasoning performance while reducing training costs by >2000x to roughly \1
and training time by >450x to around 20 minutes. Furthermore, when applied to
lightly RL-trained models (e.g., within 1 hour on 2 GPUs), it enables reasoning
performance such as 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23 for only
around 1 additional cost. Surprisingly, the reasoning abilities extracted via
SAEs are potentially both generalizable and modular. Generality means abilities
extracted from one dataset still elevate performance on a larger and
overlapping corpus. Modularity means abilities extracted from Qwen or Qwen-Math
can be attached to the R1-Distill model at test time, without any retraining,
and yield comparable gains. Extensive ablations validate these findings and all
artifacts are fully open-sourced.
SAE-Tuning efficiently elicits strong reasoning in language models by leveraging sparse autoencoders, enabling cost-effective performance gains without extensive retraining.
sparse autoencoder tuning SAE-Tuning reasoning models verification sparse autoencoders supervised fine-tuning RL post-training Pass@1 AIME24 AMC23 generality modularity R1-Distill Qwen Qwen-Math