Reward-Informed Sparse Autoencoders and the Solution-Completeness Confound
Sparse autoencoders (SAEs) decompose language-model activations into sparse, interpretable features, and an appealing way to aim them at reasoning is to curate their data with a signal reinforcement learning already produces: the reward. We build such a reward-informed SAE (RI-SAE): we split GRPO trajectories into high-reward ("good") and low-reward ("bad") reasoning continuations, train a standard JumpReLU SAE on their activations, and then ask what the resulting good/bad separation actually measures. On Llama-3.1-8B a sparse subset of the 16,384 features does separate the classes (silhouette 0.79 on the selected features versus 0.005 for the full code), but a control battery shows the s...
| Authors | Tanvi Nagilla, Alexander Jameson, Daniel Manta et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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