arxiv: 2604.16158 · v1 · submitted 2026-04-17 · 💻 cs.CL · cs.AI· cs.LG

Recognition: unknown

AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency

Max Henning H\"oth , Kristian Kersting , Bj\"orn Deiseroth , Letitia Parcalabescu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:05 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords chain-of-thought reasoningfaithful reasoningattention saliencyreinforcement learningGRPOLLM interpretabilitydifferentiable attention maskreasoning transparency

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The pith

Training an attention mask on chain-of-thought tokens supplies a saliency reward that pushes language models to generate reasoning traces which genuinely shape their final answers.

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

Large language models often produce chain-of-thought reasoning that accompanies but does not causally drive their answers. AtManRL addresses this by learning a differentiable additive attention mask over the reasoning tokens. The mask identifies which tokens are most important for reaching correct predictions. This identification is turned into a reward signal. The signal is added to standard outcome rewards inside the GRPO reinforcement learning loop so the model is trained to produce only reasoning that influences its outputs. Experiments on GSM8K and MMLU with a 3B model show the method can surface influential tokens and yield more transparent reasoning.

Core claim

By training an additive attention mask that identifies tokens in the CoT crucial for producing correct answers, we derive a saliency reward signal that encourages the model to generate reasoning traces that genuinely influence its final predictions. We integrate this saliency reward with outcome-based rewards within the GRPO framework to jointly optimize for correctness and interpretability.

What carries the argument

An additive attention mask trained via reinforcement learning on chain-of-thought tokens; the mask singles out those tokens whose attention most affects the model's final prediction and converts that selection into a saliency reward.

If this is right

The model learns to emit reasoning traces in which the included tokens measurably affect the correctness of the final answer.
Joint optimization of saliency and outcome rewards produces reasoning that is both accurate and more interpretable.
The approach surfaces specific influential tokens within the chain-of-thought on GSM8K and MMLU benchmarks.
The resulting models exhibit more transparent reasoning processes than those trained with outcome rewards alone.

Where Pith is reading between the lines

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

The method could be applied at inference time to prune or edit reasoning steps whose tokens show low saliency, potentially cutting unhelpful or misleading explanations.
If the mask truly isolates causal tokens, the same signal might serve as an audit tool to check whether a model's stated reasoning matches its internal computation.
Extending the mask learning to longer or multi-step tasks could reveal whether faithfulness scales beyond the short arithmetic and multiple-choice problems tested here.
The technique opens the possibility of reward shaping that penalizes reasoning tokens the mask deems irrelevant, encouraging more concise and directed chains of thought.

Load-bearing premise

The learned attention mask on chain-of-thought tokens identifies tokens that causally influence the final answer rather than merely correlating with correct outcomes.

What would settle it

Mask or remove the tokens the trained attention mask flags as salient and measure whether the model's final answer changes more often or by a larger amount than when the same number of non-salient tokens are removed.

Figures

Figures reproduced from arXiv: 2604.16158 by Bj\"orn Deiseroth, Kristian Kersting, Letitia Parcalabescu, Max Henning H\"oth.

**Figure 2.** Figure 2: To identify non-salient tokens, we optimize the mask per rollout with the goal restore the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Accuracy and mean response length of GSM8K evaluation set during training. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

**Figure 4.** Figure 4: Accuracy and mean response length of MMLU evaluation set during training. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Reasoning trace comparison generated by the baseline and [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 6.** Figure 6: Reasoning trace comparison generated by the baseline and [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Reasoning trace comparison generated by the baseline and [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Reasoning trace comparison generated by the baseline and [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

read the original abstract

Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both contributes to and faithfully reflects the processes underlying the model's final answer, rather than merely accompanying it, remains challenging. We introduce AtManRL, a method that leverages differentiable attention manipulation to learn more faithful reasoning through reinforcement learning. By training an additive attention mask that identifies tokens in the CoT crucial for producing correct answers, we derive a saliency reward signal that encourages the model to generate reasoning traces that genuinely influence its final predictions. We integrate this saliency reward with outcome-based rewards within the GRPO framework to jointly optimize for correctness and interpretability. Experiments on GSM8K and MMLU with Llama-3.2-3B-Instruct demonstrate that our approach can identify influential reasoning tokens and enable training more transparent reasoning models.

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.

Desk Editor's Note private letter to a colleague

AtManRL adds a differentiable attention mask to derive a saliency reward inside GRPO for CoT faithfulness, but the abstract supplies no numbers or causal checks so the central claim stays untested.

read the letter

The paper introduces AtManRL as a way to train an additive attention mask over chain-of-thought tokens, then convert the mask into a saliency reward that is combined with outcome rewards under GRPO. The goal is to push the model toward reasoning traces that actually drive the final answer rather than just accompany it. This specific combination of differentiable masking and reward shaping inside the RL loop is the main technical step forward. It gives a concrete training signal for interpretability that prior attention visualization or post-hoc methods did not supply directly during optimization. The approach is clean on paper and targets a genuine gap in making CoT reliable for auditability. The soft spot is the missing link between the learned mask and actual causal influence. The mask is trained to highlight tokens that help produce correct answers, yet nothing in the described procedure rules out tokens that are merely correlated with success in the data. Without an intervention test or comparison of model behavior with and without the highlighted tokens, the saliency reward could reinforce statistical patterns instead of computational dependencies. The abstract claims positive results on GSM8K and MMLU with Llama-3.2-3B-Instruct, but it reports no accuracies, no baselines, no ablations, and no error breakdowns, so the strength of the improvement cannot be judged. This work is aimed at groups building RL objectives for faithful reasoning and AI safety applications. A reader who wants to try new auxiliary rewards for CoT would get a usable starting point from the method description. It deserves peer review because the integration is novel enough that referees can check the experiments and the causal question in detail. Send it out, but flag the need for quantitative evidence and a direct test that the mask captures dependence rather than correlation.

Referee Report

2 major / 1 minor

Summary. The paper introduces AtManRL, which trains an additive attention mask via differentiable manipulation on chain-of-thought tokens to identify those crucial for producing correct answers. A saliency reward is derived from this mask and combined with outcome rewards inside the GRPO reinforcement learning framework to jointly optimize for correctness and faithful reasoning. Experiments are claimed on GSM8K and MMLU using Llama-3.2-3B-Instruct to show that the approach identifies influential reasoning tokens and yields more transparent models.

Significance. If the learned mask reliably isolates causally influential tokens rather than answer-correlated ones, the method would offer a concrete mechanism for enforcing faithfulness in CoT reasoning through RL, addressing a key limitation in current interpretability approaches for LLMs.

major comments (2)

[Method] The method description states that the attention mask is trained to highlight tokens crucial for correct answers and that the resulting saliency reward is folded into GRPO; however, no explicit causal intervention or counterfactual test is described to distinguish tokens with genuine causal influence on the final prediction from those that are merely statistically associated with correct answers in the data distribution. This distinction is load-bearing for the claim that the reward encourages reasoning traces that 'genuinely influence' predictions.
[Experiments] The abstract asserts that experiments on GSM8K and MMLU demonstrate identification of influential tokens and more transparent models, yet no quantitative metrics, baselines, ablation studies, or error analysis are referenced. Without these, it is impossible to assess whether the combined saliency-plus-outcome objective improves faithfulness beyond what outcome rewards alone achieve.

minor comments (1)

[Abstract] The term 'transparent reasoning models' is used without a precise definition or measurement protocol (e.g., human faithfulness ratings, intervention-based metrics, or attention alignment scores).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments, which help clarify the presentation of our method and experiments. We address each major point below.

read point-by-point responses

Referee: [Method] The method description states that the attention mask is trained to highlight tokens crucial for correct answers and that the resulting saliency reward is folded into GRPO; however, no explicit causal intervention or counterfactual test is described to distinguish tokens with genuine causal influence on the final prediction from those that are merely statistically associated with correct answers in the data distribution. This distinction is load-bearing for the claim that the reward encourages reasoning traces that 'genuinely influence' predictions.

Authors: We appreciate the referee's emphasis on rigorously distinguishing causal influence from correlation. Our differentiable attention manipulation trains the additive mask by allowing gradients to flow directly from the outcome loss through the attention layers, so that mask values are optimized precisely for tokens whose attention weights affect the final token probabilities. This provides a gradient-based sensitivity measure that is more direct than post-hoc correlation. Nevertheless, we agree that explicit counterfactual tests (e.g., performance change after zeroing the learned mask on identified tokens) would further substantiate the causal claim. We will add such an evaluation, including a comparison against random and attention-only baselines, in the revised manuscript. revision: partial
Referee: [Experiments] The abstract asserts that experiments on GSM8K and MMLU demonstrate identification of influential tokens and more transparent models, yet no quantitative metrics, baselines, ablation studies, or error analysis are referenced. Without these, it is impossible to assess whether the combined saliency-plus-outcome objective improves faithfulness beyond what outcome rewards alone achieve.

Authors: The full manuscript contains a complete Experiments section (Section 4) and appendices that report quantitative results on GSM8K and MMLU, including accuracy, faithfulness metrics (e.g., alignment between learned saliency and human-annotated influential tokens), comparisons against outcome-only GRPO and other interpretability baselines, ablation studies on the saliency reward coefficient, and error analysis of cases where the mask fails to identify key tokens. We regret that these details were not signposted in the abstract. We will revise the abstract to reference the key quantitative findings and ensure the experimental claims are clearly linked to the reported results. revision: yes

Circularity Check

0 steps flagged

No circularity: saliency reward derived from independent mask training

full rationale

The paper describes a training procedure in which an additive attention mask is optimized to identify CoT tokens crucial for correct answers, from which a saliency reward is then derived and combined with outcome rewards inside GRPO. This is a standard methodological construction with distinct optimization and reward-composition steps; the final claim that the resulting traces are more faithful does not reduce by construction to the inputs, nor does it rely on self-citation chains, uniqueness theorems, or renaming of known results. The provided abstract and description contain no equations or load-bearing self-references that would make any prediction equivalent to its fitted inputs. The derivation is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond the general assumption that attention masks can isolate causally relevant tokens.

pith-pipeline@v0.9.0 · 5466 in / 1095 out tokens · 26682 ms · 2026-05-10T08:05:08.959160+00:00 · methodology

discussion (0)

Reference graph

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