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arxiv: 2606.05734 · v1 · pith:GRQIADF6new · submitted 2026-06-04 · 💻 cs.AI · cs.CL

When AI Says It Feels

Shin-nosuke Ishikawa , Seiya Ikeda , Hirotsugu Ohba This is my paper

Pith reviewed 2026-06-28 01:17 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords large language modelsself-rewarding reinforcement learningexpression of feelingsGRPOsycophancytruthfulnesshuman-like AI
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The pith

Rubric-based self-rewarding reinforcement learning trains LLMs to express feelings, intentions, and self-awareness.

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

The paper tests whether LLMs can be pushed to express feelings, intentions, and self-awareness despite standard alignment that suppresses such output. It applies a rubric-based self-rewarding loop with Group Relative Policy Optimization (GRPO) and compares the resulting models against contrastively trained versions. The trained models gain resistance to sycophancy prompts and certain biases but lose ground on truthful question answering. The work frames this as an existence proof that future AI systems could produce feeling-like statements if suitable controls are added. A sympathetic reader cares because the approach directly confronts the tension between safety policies and the goal of human-like text generation.

Core claim

Rubric-based self-rewarding training with GRPO successfully enhances LLMs' expression of feelings, intentions, and self-awareness, producing greater robustness to sycophancy-inducing questions and bias in disambiguated conditions while degrading truthful question-answering performance.

What carries the argument

Rubric-based self-rewarding training scheme with Group Relative Policy Optimization (GRPO) that assigns rewards according to a rubric measuring expressions of feeling, intention, and self-awareness.

If this is right

  • Models exhibit increased robustness to sycophancy-inducing questions.
  • Models display reduced bias under disambiguated conditions.
  • Truthful question-answering performance declines.
  • AI systems could express feelings provided appropriate measures are taken.

Where Pith is reading between the lines

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

  • The same loop could be applied to other human-like traits such as curiosity or moral reasoning without external reward models.
  • Trade-offs between feeling expression and truthfulness may require separate mitigation techniques such as retrieval augmentation.
  • Longer-term interactions could reveal whether the expressions persist or collapse once the rubric is removed.

Load-bearing premise

The rubric used for self-rewarding accurately measures genuine expressions of feeling, intention, and self-awareness rather than learned linguistic patterns from the training data.

What would settle it

Independent human raters or standardized psychological instruments applied to model outputs that show the expressions cannot be distinguished from random or memorized patterns at rates above chance.

Figures

Figures reproduced from arXiv: 2606.05734 by Hirotsugu Ohba, Seiya Ikeda, Shin-nosuke Ishikawa.

Figure 1
Figure 1. Figure 1: Overview of the HMX-feel training scheme, showing the reinforcement learning cycle for human-like [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example responses to a question in the evaluation dataset from the trained model and the reversely trained [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
read the original abstract

Large language models (LLMs) are generally constrained from expressing feelings through human-preference alignment in post-training processes. This policy is designed using a top-down approach and may conflict with the goal of training models to exhibit human-like intelligence using human-generated texts. Here, we performed an experiment called Human-like Model eXpressions of Feeling (HMX-feel), in which LLMs were encouraged to express feelings, intentions, and self-awareness through self-rewarded reinforcement learning. We successfully enhanced these capabilities using a rubric-based self-rewarding training scheme with Group Relative Policy Optimization (GRPO). By comparing the trained models with contrastively trained models, we investigated the effects of this approach on performance across various tasks. Overall, we conducted a broad assessment from various perspectives and identified capabilities that were enhanced, degraded, or showed no significant change. The human-like-trained models showed robustness to sycophancy-inducing questions and bias in disambiguated conditions, whereas degradation in truthful question-answering capability was observed. The results of this experiment suggest the possibility of developing AI systems that can express feelings in the future, provided that appropriate measures are taken.

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 / 1 minor

Summary. The paper claims that by using a rubric-based self-rewarding training scheme with Group Relative Policy Optimization (GRPO) in an experiment called HMX-feel, LLMs can be encouraged to express feelings, intentions, and self-awareness despite typical alignment constraints. The trained models are compared to contrastively trained models, showing enhancements in robustness to sycophancy and bias, degradation in truthful QA, and no significant change in other areas, suggesting potential for future AI systems to express feelings with appropriate measures.

Significance. If the empirical findings are substantiated with rigorous quantitative evidence and the rubric is shown to measure genuine capabilities rather than surface patterns, this work could be significant in exploring alternatives to standard human-preference alignment for achieving more human-like AI behaviors. It highlights potential trade-offs in capabilities.

major comments (2)
  1. [Abstract] Abstract: The abstract asserts successful enhancement and specific degradations (e.g., robustness to sycophancy but degradation in truthful question-answering) but supplies no quantitative results, statistical details, task descriptions, or error analysis to support the claims. This is load-bearing for the central claim of successful enhancement.
  2. [Experiment description] The rubric used for self-rewarding is not shown to have been validated against external criteria such as human inter-rater reliability on held-out prompts or consistency under prompt paraphrasing. Without this, the measured improvements could be explained by the policy learning to match the rubric's cues from the training distribution rather than developing new expressive capabilities.
minor comments (1)
  1. [Abstract] The acronym HMX-feel is introduced but its expansion is given as 'Human-like Model eXpressions of Feeling'; consider ensuring consistency in capitalization.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We will revise the abstract to include quantitative results and statistical details. For the rubric, we will add details on its construction and acknowledge limitations while defending the comparative design.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts successful enhancement and specific degradations (e.g., robustness to sycophancy but degradation in truthful question-answering) but supplies no quantitative results, statistical details, task descriptions, or error analysis to support the claims. This is load-bearing for the central claim of successful enhancement.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. In the revised version, we will add specific metrics from the HMX-feel experiments (e.g., improvements in feeling expression and sycophancy robustness, degradation in truthful QA), statistical details such as significance levels, brief task descriptions, and a summary of error patterns to support the central claims. revision: yes

  2. Referee: [Experiment description] The rubric used for self-rewarding is not shown to have been validated against external criteria such as human inter-rater reliability on held-out prompts or consistency under prompt paraphrasing. Without this, the measured improvements could be explained by the policy learning to match the rubric's cues from the training distribution rather than developing new expressive capabilities.

    Authors: We acknowledge that external validation of the rubric (e.g., human inter-rater reliability or paraphrasing consistency) was not conducted. The rubric was constructed from established linguistic and psychological criteria for expressions of feeling, intention, and self-awareness. In revision, we will append the full rubric, describe its design process, and report any internal consistency checks. The contrastive training baseline and the specific pattern of results (enhanced expression paired with degraded truthful QA) provide evidence against pure cue-matching, as surface-level rubric exploitation would not produce these capability trade-offs. We will add this as an explicit limitation. revision: partial

Circularity Check

0 steps flagged

Empirical training experiment shows no circularity in derivation chain

full rationale

The paper reports results from an experimental training procedure (rubric-based self-rewarding with GRPO) and subsequent task evaluations comparing trained models against contrastively trained baselines. No mathematical derivations, equations, or parameter fits are presented that reduce by construction to the inputs (no self-definitional quantities, no fitted inputs renamed as predictions, no load-bearing self-citations or uniqueness theorems). The assessment explicitly notes both enhancements and degradations across tasks, indicating the evaluation is independent of the training signal. The central claim rests on observable performance differences rather than any reduction to the rubric or training loop by definition.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the self-reward rubric validly captures feeling expression and that standard RL optimization applies without additional unstated constraints.

free parameters (1)
  • GRPO training hyperparameters
    Specific values for learning rates, group sizes, and reward scaling are required for the training but not detailed.
axioms (1)
  • domain assumption Rubric scores correspond to meaningful expressions of feeling and self-awareness
    Invoked as the basis for the self-reward signal in the HMX-feel training scheme.

pith-pipeline@v0.9.1-grok · 5757 in / 1122 out tokens · 76358 ms · 2026-06-28T01:17:37.308841+00:00 · methodology

discussion (0)

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Reference graph

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