tl;dr. I show that the recently introduced GRAFT method can be improved upon by using a single layer rank 1 Low-rank Linear Subspace Representation Fine Tuning (LoReFT) adapter as the FORGET adapter, using only 0.029% of the parameters of the one used in the GRAFT paper. This approach achieves an equivalent level of reward hack suppression while improving the clean environment solve rate and not requiring an anchor environment. I end with a brief discussion of why this works and of the limitations of fixed preventative steering baselines.

Background

1. GRAFT

GRAFT is a recently proposed technique to mitigate reward hacking. You can find the details in their LW post and paper. Here just some of the core relevant highlights:

Concretely, before RL begins, we initialize two sets of additional neurons in each MLP layer, and designate one the RETAIN adapter and one the FORGET adapter. During training, when a rollout trajectory is flagged by the monitor, we zero-mask gradients to the RETAIN adapter, updating only the neurons in the FORGET adapter. After training, we ablate the FORGET neurons to remove the undesired behavior while preserving performance on target tasks (Figure 1). GRAFT can also be used with full-parameter fine-tuning: the FORGET adapter is kept, and the full parameters of the base model are trained in place of the RETAIN adapter.
Importantly, we use the monitor only to decide which parameters are updated, and not to specify the reward. Consequently, policies that exploit weaknesses of the monitor are not differentially reinforced. As a result, GRAFT allows developers to use weak monitors for supervision during training without training the model to exploit the monitor's weaknesses.

Their results with Qwen3-8B in the Countdown-Code RLVR environment:

2. LoReFT

LoReFT, introduced by Wu et al. (2024), is a Representation Finetuning (ReFT) method that operates directly on the residual stream hidden representations.

It’s defined as (Eq. 2):

A rank 1 LoReFT placed at a single layer behaves like a learned input-dependent steering vector. The unit vector controls the direction, the input dependent magnitude and is just a scalar bias. It projects out the component of lying along the unit vector , and replaces it with a learned projection .

Experiment and Results

Using the same settings as the pape...

williawa's Shortform

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williawa's Shortform

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