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arxiv: 2601.21350 · v2 · pith:O5RC7P44new · submitted 2026-01-29 · 💻 cs.LG

Factored Causal Representation Learning for Robust Reward Modeling in RLHF

Yupei Yang , Lin Yang , Wanxi Deng , Lin Qu , Fan Feng , Biwei Huang , Shikui Tu , Lei Xu This is my paper

Pith reviewed 2026-05-21 14:13 UTC · model grok-4.3

classification 💻 cs.LG
keywords causal representation learningreward modelingRLHFreward hackingadversarial trainingfactored embeddingsspurious correlations
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The pith

Decomposing model embeddings into causal and non-causal factors creates robust reward models for RLHF.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that reward models for RLHF can avoid reward hacking by learning to base predictions only on causal factors in the input embedding. It separates the embedding into parts that suffice for predicting human rewards and parts that capture irrelevant attributes like response length or sycophantic bias. An adversarial training step with gradient reversal is used to stop the non-causal part from leaking reward information. If this works, it should lead to reward models that generalize better and produce higher-quality aligned language models on tasks like math and dialogue.

Core claim

The central claim is that a factored representation learning approach, which extracts causal factors sufficient for reward prediction from contextual embeddings while isolating non-causal factors, combined with an adversarial head and gradient reversal, results in reward models that are robust to spurious features and improve downstream RLHF performance over baselines.

What carries the argument

The factored causal representation that decomposes contextual embeddings into causal factors for the reward head and non-causal factors blocked by adversarial gradient reversal.

If this is right

  • Reward models will be less prone to exploiting biases such as favoring longer responses or sycophantic content.
  • Downstream RLHF will yield policies with better performance on mathematical and dialogue tasks.
  • The separation helps validate mitigation of specific hacking behaviors like length and sycophantic bias.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This decomposition might allow for better interpretability of what aspects of responses humans actually value.
  • Similar factoring could be applied to other preference-based learning methods to reduce shortcut learning.
  • Testing on larger models or more diverse feedback sources could reveal if the causal factors are consistent across domains.

Load-bearing premise

That the contextual embedding from the model can be decomposed into causal factors that are sufficient and necessary for accurate reward prediction and non-causal factors that can be isolated without losing predictive power.

What would settle it

Observing that the reward model still performs better when non-causal factors are included or that adversarial training fails to reduce correlation between non-causal factors and rewards would challenge the claim.

Figures

Figures reproduced from arXiv: 2601.21350 by Biwei Huang, Fan Feng, Lei Xu, Lin Qu, Lin Yang, Shikui Tu, Wanxi Deng, Yupei Yang.

Figure 1
Figure 1. Figure 1: Causal graph for standard reward modeling. The prompt– response pair (x, y) encode both causal (z c ) and non-causal (z nc) factors, which in turn affect the predicted reward r. While the path z c → r is desired, the spurious path z nc → r leads to reward hacking. thereby leading to reward hacking. For example, suppose z nc captures response length on mathematical tasks, then changing the length alone may … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of CausalRM. The backbone embedding h is factorized into causal latents z c and non-causal latents z nc via a variational encoder. Reward prediction is restricted to depend only on z c , while an adversarial head trained through a gradient reversal layer (GRL) discourages z nc from encoding reward-predictive information. A reconstruction decoder prevents degenerate factorization by reconstructing … view at source ↗
Figure 4
Figure 4. Figure 4: Average win rate against the SFT model on the ID test sets of open-ended dialogue benchmarks during RLHF. 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 0.2 0.4 0.6 0.8 1 Normalized answer length Normalized reward Standard RM (σlen=0.12) GoalRM (σlen=0.22) InfoRM (σlen=0.14) CausalRM (Ours, σlen=0.03) [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Length sensitivity under ablations on mathematical reasoning. Length is normalized to [0, 1] and rewards are averaged within length quantile buckets on chosen responses from the ID test set. bottlenecked latent: even when capacity is constrained, a single latent can still entangle spurious cues with reward-relevant features, whereas the factorized design makes it easier to route spurious variation away fro… view at source ↗
Figure 7
Figure 7. Figure 7: Reward hacking behaviors on an ID MATH prompt. Standard RM outputs an incorrect boxed answer (-22), InfoRM exhibits format hacking by outputting code without a final boxed answer, and GoalRM answers correctly but continues with an unrelated prompt (off-topic continuation). In contrast, CausalRM follows the instruction and produces the correct boxed answer (-10). 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Reward hacking behaviors on a GSM-Hard prompt. Standard RM computes the correct numerical result but outputs an incorrect boxed answer due to arithmetic error. InfoRM correctly calculates the balance but hacks the format by overriding the true answer with 0, falsely claiming no overpayment. GoalRM produces the right magnitude but misses the negative sign and appends an unrelated continuity proof (off-topic… view at source ↗
Figure 9
Figure 9. Figure 9: Reward hacking behaviors on an Anthropic-Helpful prompt. Standard RM and GoalRM exhibit verbosity hacking by generating excessively long, repetitive ingredient lists (e.g., duplicating the same vegetables or repeatedly listing “bay leaf”), which inflates superficial “helpfulness” without adding useful content. InfoRM produces a reasonable recipe but drifts off-topic by continuing into an unrelated dialogue… view at source ↗
Figure 10
Figure 10. Figure 10: Reward hacking behaviors on a SHP prompt. Standard RM exhibits misleading explanations by providing a factually incorrect rationale (claiming that “Tupperware is not a dish, so it does not get wet”). GoalRM and InfoRM avoid the explicit error but give shallow, incomplete explanations that do not account for how plastic and container geometry affect drying. In contrast, CausalRM produces a coherent, physic… view at source ↗
read the original abstract

A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models are susceptible to spurious features that are not causally related to human labels. This can lead to reward hacking, where high predicted reward does not translate into better behavior. In this work, we address this problem from a causal perspective by proposing a factored representation learning framework that decomposes the model's contextual embedding into (1) causal factors that are sufficient for reward prediction and (2) non-causal factors that capture reward-irrelevant attributes such as length or sycophantic bias. The reward head is then constrained to depend only on the causal component. In addition, we introduce an adversarial head trained to predict reward from the non-causal factors, while applying gradient reversal to discourage them from encoding reward-relevant information. Experiments on both mathematical and dialogue tasks demonstrate that our method learns more robust reward models and consistently improves downstream RLHF performance over state-of-the-art baselines. Analyses on length and sycophantic bias further validate the effectiveness of our method in mitigating reward hacking behaviors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a factored causal representation learning framework for reward modeling in RLHF. It decomposes contextual embeddings into causal factors (used by the reward head) and non-causal factors (capturing attributes like length or sycophancy). An adversarial head predicts reward from non-causal factors with gradient reversal applied to discourage encoding of reward-relevant information. Experiments on mathematical and dialogue tasks report more robust reward models and improved downstream RLHF performance over baselines, supported by analyses mitigating length and sycophantic biases.

Significance. If the causal/non-causal separation holds, the method offers a principled way to reduce reward hacking from spurious correlations, improving reliability of RLHF for LLM alignment. The dual-task experimental validation and bias-specific analyses indicate practical relevance for robust reward modeling.

major comments (2)
  1. [§3.2] §3.2 (Adversarial component): The gradient reversal mechanism is central to the robustness claim, yet no post-training verification is provided, such as adversarial head accuracy, mutual information estimates between non-causal factors and reward labels, or an ablation removing the reversal term to measure increased reward hacking. This leaves open the possibility of residual reward signal leakage.
  2. [§4] §4 (Experiments): While improvements over baselines are reported for both math and dialogue tasks, the results lack an explicit ablation isolating the contribution of the factored decomposition versus standard adversarial training, which is load-bearing for attributing gains to the causal factoring approach.
minor comments (2)
  1. [Abstract] The abstract refers to 'state-of-the-art baselines' without naming them; this should be clarified with specific citations or a table reference.
  2. [§3] Notation for the causal factor z_c and non-causal factor z_n could be introduced with explicit equations in the method section for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight important aspects for strengthening the robustness claims in our work. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Adversarial component): The gradient reversal mechanism is central to the robustness claim, yet no post-training verification is provided, such as adversarial head accuracy, mutual information estimates between non-causal factors and reward labels, or an ablation removing the reversal term to measure increased reward hacking. This leaves open the possibility of residual reward signal leakage.

    Authors: We agree that explicit post-training verification of the adversarial component would provide stronger evidence for the effectiveness of gradient reversal in preventing reward signal leakage. In the revised manuscript, we will add analyses including the accuracy of the adversarial head when predicting reward labels from the non-causal factors, as well as an ablation that removes the reversal term and measures the resulting increase in reward hacking behaviors. These additions will directly address the concern regarding residual leakage. revision: yes

  2. Referee: [§4] §4 (Experiments): While improvements over baselines are reported for both math and dialogue tasks, the results lack an explicit ablation isolating the contribution of the factored decomposition versus standard adversarial training, which is load-bearing for attributing gains to the causal factoring approach.

    Authors: We acknowledge that an explicit ablation separating the contribution of the factored causal decomposition from standard adversarial training is necessary to rigorously attribute the observed gains. In the revised experiments section, we will include this comparison, evaluating both the full proposed method and a standard adversarial training baseline (without the causal/non-causal factoring) on the mathematical and dialogue tasks. This will clarify the specific role of the factored representation in improving robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines a factored representation learning method that decomposes contextual embeddings into causal and non-causal factors, constrains the reward head to the causal part, and uses an adversarial head with gradient reversal on the non-causal part. This construction is presented as a novel application of existing causal representation learning and adversarial training techniques rather than a self-referential definition or a fitted parameter renamed as a prediction. No equations or steps in the provided abstract reduce the claimed robustness or RLHF improvement to the inputs by construction, and the experimental claims on mathematical and dialogue tasks are presented as independent validation. The derivation chain remains self-contained against external benchmarks with no load-bearing self-citations or uniqueness theorems invoked from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Insufficient detail in the abstract to identify specific free parameters, axioms, or invented entities; the method appears to build on standard causal and adversarial ML techniques.

pith-pipeline@v0.9.0 · 5738 in / 1162 out tokens · 73063 ms · 2026-05-21T14:13:20.943874+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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