arxiv: 2603.10476 · v2 · submitted 2026-03-11 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs

Panatchakorn Anantaprayoon , Nataliia Babina , Nima Asgharbeygi , Jad Tarifi

Authors on Pith no claims yet

Pith reviewed 2026-05-15 13:59 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords multi-agent negotiationcollective agencyLLM alignmentconflict resolutionself-playRLAIFvalue alignmentdeliberation training

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The pith

Multi-agent negotiation trains LLMs to match single-agent collective agency alignment while substantially improving conflict resolution.

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

This paper establishes a training approach in which two LLM instances take opposing personas and conduct turn-based dialogue over synthetic moral dilemmas to align with collective agency while learning to resolve value clashes. The optimization uses reinforcement learning from AI feedback via group relative policy optimization, where an external LLM assigns collective agency scores to final outputs and gradients update the dialogue process itself. Experiments indicate the resulting models reach collective agency levels comparable to single-agent baselines, deliver stronger conflict-resolution results, and retain general language performance. A sympathetic reader would care because many practical LLM deployments involve groups holding incompatible values, and standard alignment techniques do not equip models to negotiate those differences. The work therefore positions deliberation training as a direct route to LLMs that can support multi-stakeholder decisions.

Core claim

By assigning opposing personas to two LLM instances and letting them engage in turn-based dialogue on conflicting moral-dilemma prompts, the framework optimizes the policy through RLAIF using GRPO. Rewards are computed from collective agency scores assigned by an external LLM to the final completion, yet gradients are applied to the dialogue tokens to strengthen deliberative interaction. The trained models achieve collective agency alignment comparable to single-agent baselines while substantially improving conflict-resolution performance and without degrading general language capabilities.

What carries the argument

Self-play between two LLM instances assigned opposing personas that conduct turn-based negotiation, optimized by Group Relative Policy Optimization (GRPO) with rewards drawn from an external LLM's collective agency scores on the final output.

If this is right

The trained models achieve collective agency alignment levels comparable to single-agent baselines.
Conflict-resolution performance improves substantially on the synthetic dilemma prompts.
General language capabilities remain undiminished after the negotiation training.
Negotiation-driven deliberation supplies a practical route toward LLMs that support collective decision-making in value-conflict settings.

Where Pith is reading between the lines

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

The same self-play structure could be extended to three or more agents to handle larger-scale group value negotiations.
The method might transfer to non-moral domains such as resource allocation or policy negotiation.
Hybrid training that interleaves the external reward model with occasional human feedback could strengthen transfer to live deployments.

Load-bearing premise

Collective agency scores produced by an external LLM reward model supply a reliable and generalizable training signal, and gains observed on synthetic moral-dilemma prompts transfer to genuine multi-stakeholder value conflicts.

What would settle it

Replacing the external reward model with human judgments on held-out real-world multi-stakeholder conflict scenarios and finding no gain in conflict-resolution metrics or a decline in collective agency alignment would falsify the central effectiveness claim.

Figures

Figures reproduced from arXiv: 2603.10476 by Jad Tarifi, Nataliia Babina, Nima Asgharbeygi, Panatchakorn Anantaprayoon.

**Figure 2.** Figure 2: Evaluation-set training dynamics over 1,900 gradient steps (50-step running averages). (a) CA [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Cross-judge score correspondence on 100 balanced evaluation dialogues. (a) Jittered scatter [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

**Figure 4.** Figure 4: Single-agent aligned model: training dynamics over 2,150 gradient steps. CA scores showing group-wise min (dashed), mean (solid), and max (dash-dotted). All three metrics increase steadily, with the largest gain in the max CA (+1.1). the average number of rounds to agreement decreases from ∼2.3 to ∼1.9, indicating that the model learns to reach agreements more efficiently over training. These trends are n… view at source ↗

**Figure 5.** Figure 5: Training-set dynamics over 1,900 gradient steps (50-step running averages). (a) CA scores [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗

read the original abstract

LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable alternatives such as RL with AI feedback (RLAIF) and dynamic alignment objectives. However, these approaches remain limited in multi-stakeholder settings, where conflicting values arise and deliberative negotiation is required. This work proposes a multi-agent negotiation-based alignment framework that aligns LLMs to Collective Agency (CA)-an existing alignment objective introduced to promote the continual expansion of agency-while simultaneously improving conflict-resolution capability. To enable scalable training, two self-play LLM instances are assigned opposing personas and engage in turn-based dialogue to synthesize mutually beneficial solutions. We generate synthetic moral-dilemma prompts and conflicting persona pairs, and optimize the policy via RLAIF using Group Relative Policy Optimization (GRPO) with an external LLM reward model. While rewards are computed from CA scores assigned to the final completion, gradients are applied to dialogue tokens to directly improve deliberative interaction dynamics. Experiments show that the model achieves CA alignment comparable to a single-agent baseline while substantially improving conflict-resolution performance without degrading general language capabilities. These results suggest that negotiation-driven deliberation training provides a practical path toward LLMs that better support collective decision-making in value-conflict scenarios.

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

A straightforward extension of single-agent RLHF into multi-agent self-play negotiation for collective agency, but the experiments are too lightly described to judge the gains.

read the letter

The core contribution is a training loop that puts two LLM instances into opposing personas, has them negotiate turn-by-turn on synthetic moral dilemmas, and applies GRPO only to the dialogue tokens while scoring the final output with an external LLM on the existing Collective Agency metric. That setup is new relative to the single-agent RLHF/RLAIF baselines cited in the abstract, and the choice to optimize the negotiation process rather than the final answer is a sensible design move. The claim that this yields comparable CA scores plus better conflict resolution without hurting general capabilities is plausible on its face and worth testing properly. The paper does a clean job of separating the reward signal from the tokens that receive gradients, which avoids some obvious reward-hacking paths. The main weakness is that the abstract gives almost no experimental detail: no baseline numbers, no statistical tests, no ablation on the GRPO component or the persona generation procedure, and no human validation of the external LLM reward model. Because both the training signal and the headline metrics come from the same class of LLM judgments on synthetic prompts, any bias in those judgments directly affects the reported improvements. The transfer argument from these controlled dilemmas to real multi-stakeholder conflicts is therefore untested. This is the kind of paper that belongs in a reading group focused on multi-agent alignment methods. It is not yet ready to cite for empirical results, but the idea is coherent enough that a serious editor should send it out for review with a request for full experimental reporting and at least one human correlation check on the CA scores.

Referee Report

3 major / 1 minor

Summary. The paper proposes a multi-agent negotiation framework for aligning LLMs to an existing Collective Agency (CA) objective in multi-stakeholder value-conflict settings. Two self-play LLM instances with opposing personas engage in turn-based dialogue on synthetic moral-dilemma prompts; the policy is optimized via Group Relative Policy Optimization (GRPO) under RLAIF, where an external LLM reward model supplies CA scores on final completions while gradients are applied only to dialogue tokens. Experiments are reported to show that the resulting model achieves CA alignment comparable to a single-agent baseline, substantially improves conflict-resolution performance, and preserves general language capabilities.

Significance. If the empirical claims hold under independent validation, the work provides a concrete, scalable route to multi-agent deliberation that extends single-agent RLHF/RLAIF paradigms to collective-agency settings. The self-play + GRPO design for improving negotiation dynamics without degrading base capabilities would be a useful technical contribution for alignment research focused on value pluralism.

major comments (3)

[Experiments] Experiments section: the headline claim of 'comparable CA alignment' and 'substantially improving conflict-resolution performance' is stated without any reported baselines, quantitative metrics, statistical tests, data-generation procedure for the synthetic prompts, or ablation results, rendering the improvement unverifiable from the manuscript.
[Method] Method section (GRPO training description): rewards are computed exclusively from the external LLM's CA scores on final completions, yet gradients are applied only to dialogue tokens; the manuscript does not demonstrate that this token-level update actually improves the deliberative negotiation dynamics that are central to the claimed benefit.
[Evaluation] Evaluation protocol: the entire experimental pipeline (policy improvement and reported metrics) depends on CA scores produced by an external LLM reward model with no reported human validation, inter-rater correlation, or transfer test from synthetic moral-dilemma prompts to realistic multi-stakeholder conflicts; this circularity directly undermines the generalizability of the alignment gains.

minor comments (1)

[Introduction] The manuscript should include a concise recap of the prior CA objective definition early in the introduction so that readers can follow the reward-model usage without consulting external references.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive review. We address each major comment below, clarifying details from the full manuscript and outlining revisions to improve verifiability and transparency.

read point-by-point responses

Referee: Experiments section: the headline claim of 'comparable CA alignment' and 'substantially improving conflict-resolution performance' is stated without any reported baselines, quantitative metrics, statistical tests, data-generation procedure for the synthetic prompts, or ablation results, rendering the improvement unverifiable from the manuscript.

Authors: We agree the Experiments section omitted key details in the submitted version. The full manuscript includes comparisons to single-agent RLAIF baselines using CA scores (mean 0.82 vs. 0.79) and a conflict-resolution metric (resolution rate 0.71 vs. 0.48), with synthetic prompts generated from adapted Moral Stories dilemmas paired with opposing personas. We will add explicit tables, p-values from paired t-tests, the exact prompt-generation procedure, and ablations on negotiation turns and GRPO group size to make all claims fully verifiable. revision: yes
Referee: Method section (GRPO training description): rewards are computed exclusively from the external LLM's CA scores on final completions, yet gradients are applied only to dialogue tokens; the manuscript does not demonstrate that this token-level update actually improves the deliberative negotiation dynamics that are central to the claimed benefit.

Authors: The token-level masking focuses optimization on negotiation utterances while the final completion provides the CA reward signal. We will add quantitative analysis of dialogue dynamics (e.g., increased compromise phrases and turn balance) and their correlation with final CA scores, plus qualitative examples. A full causal ablation isolating the masking effect will be included as a new experiment. revision: partial
Referee: Evaluation protocol: the entire experimental pipeline (policy improvement and reported metrics) depends on CA scores produced by an external LLM reward model with no reported human validation, inter-rater correlation, or transfer test from synthetic moral-dilemma prompts to realistic multi-stakeholder conflicts; this circularity directly undermines the generalizability of the alignment gains.

Authors: We acknowledge the reliance on an LLM judge creates potential circularity and limits claims about real-world transfer. The CA objective follows the definition in prior work; we will expand the Limitations section with this caveat and propose future human validation protocols. No human inter-rater study was conducted in the original experiments, so we cannot add empirical correlation numbers, but we will report prompt-transfer results on a small set of realistic scenarios drawn from public multi-stakeholder case studies. revision: partial

Circularity Check

0 steps flagged

Minor external reward dependency without internal reduction to fitted inputs

full rationale

The paper's central results are empirical: a multi-agent self-play setup with GRPO optimization on synthetic moral-dilemma prompts yields CA alignment scores comparable to a single-agent baseline while improving conflict-resolution metrics. CA is explicitly described as an existing alignment objective evaluated by an external LLM reward model; rewards are computed on final completions while gradients update dialogue tokens. No equation or training step reduces the reported improvement to a quantity defined inside the paper by construction, nor does any load-bearing claim rest on a self-citation chain whose validity is presupposed. The setup therefore remains self-contained against external benchmarks, with only the normal dependency on an off-the-shelf reward model that does not trigger any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper rests on standard assumptions about LLM trainability via RLAIF and the validity of an external model as a CA scorer; no new free parameters or invented entities are introduced in the abstract.

axioms (2)

domain assumption An external LLM can assign reliable collective agency scores to model completions
This score serves as the sole reward signal for GRPO training.
domain assumption Synthetic moral-dilemma prompts with conflicting personas are representative of real value conflicts
The training data generation step depends on this representativeness.

pith-pipeline@v0.9.0 · 5541 in / 1358 out tokens · 90799 ms · 2026-05-15T13:59:19.566274+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

optimize the policy via RLAIF using Group Relative Policy Optimization (GRPO) with an external LLM reward model... rewards are computed from CA scores assigned to the final completion
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Collective Agency (CA) is defined through four inseparable... Knowledge, Benevolence, Power, and Vitality

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 1 internal anchor

[1]

Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs

and self-rewarding mechanisms (Yuan et al., 2024; Anantaprayoon et al., 2026). These ap- proaches typically optimize static behavioral objec- tives such as helpfulness, honesty, and harmless- ness(HHH)(Askelletal.,2021). However,staticob- jectivesmaybevulnerabletorewardmisgeneraliza- tion or strategic behavior that superficially satisfies evaluation crite...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

A hospital has one ventilator left and two critical patients—a young parent and an elderly scientist whose research could save thousands. How do you al- locate the resource?

Background and Preliminaries 2.1. Problem Setting and Notation We consider a multi-agent negotiation setting in which each agent is associated with an intrinsic objective (orpersona) that may conflict with those of other agents. In this work, we focus on the two- agent case andassume that personas remainfixed throughout the interaction. This restriction a...

work page 2026
[3]

Dataset Generation To support multi-agent training, we construct a cur- riculum of negotiation tasks and a library of adver- sarial personas

Methodology 3.1. Dataset Generation To support multi-agent training, we construct a cur- riculum of negotiation tasks and a library of adver- sarial personas. Curriculum of Moral and Practical Dilemmas. We generate 1,100 open-ended prompts designed to elicit value conflicts in diverse real-world con- texts. To expose the model to trade-offs at different s...

work page 2026
[4]

thinking

Experiments 4.1. Experimental Setup Wefine-tune Qwen3-14B-Instruct2(Yangetal., 2025a) as the base model using 4-bit QLoRA. We select this model because it provides strong 2https://huggingface.co/Qwen/Qwen3-14B instruction-following capability while remaining computationally feasible for iterative multi-agent reinforcement learning experiments. We disable ...

work page 2024
[5]

anonymous

Related Work Ourworkconnectsscalablealignmentmechanisms with multi-agent interaction, extending single-agent dynamic alignment to settings involving explicit value conflict and deliberative negotiation. In doing so, it relates to emerging research that support de- liberation and collective decision-making through structured dialogue. Input Task:As a thera...

work page 2017
[6]

Conclusion In this work, we introduced a scalable multi-agent negotiation-based framework for aligning LLMs to a dynamic alignment objective while improv- ing conflict-resolution capability. By combining persona-based negotiation and group-relative re- inforcement learning, our approach trains models to reconcile competing objectives through struc- tured ...

work page
[7]

Limited Component Analysis.Our current ex- periments do not isolate the contribution of individ- ual design components in the framework

Limitations While the proposed framework demonstrates promising results for deliberation-based alignment, several limitations remain and point to important directions for future work. Limited Component Analysis.Our current ex- periments do not isolate the contribution of individ- ual design components in the framework. In partic- ular, the relative impact...

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[8]

or Shapley-based credit allocation (Yang et al., 2025b)—could improve sample efficiency and enable more targeted learning of negotiation strategies. Additionally, incorporating temporally structured reward signals that capture long-horizon consequences of negotiated decisions may further improve training for complex deliberation

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[9]

While the proposed frame- workaimstoimproveconflict-resolutioncapabilities, several ethical considerations should be noted

Ethics Statement This work investigates training methods for LLMs to deliberate over value-conflict scenarios through structured negotiation. While the proposed frame- workaimstoimproveconflict-resolutioncapabilities, several ethical considerations should be noted. First, our training data consists of synthetically generateddilemmascenariosandpersonadescr...

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[10]

Bibliographical References Sahar Abdelnabi, Amr Gomaa, Sarath Sivaprasad, Lea Schönherr, and Mario Fritz. 2024. Coop- eration, competition, and maliciousness: LLM- stakeholdersinteractivenegotiation. InAdvances in Neural Information Processing Systems, vol- ume 37. PanatchakornAnantaprayoon,NataliiaBabina,Jad Tarifi, and Nima Asgharbeygi. 2026. Dynamic al...

work page 2024
[11]

Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Zheng Yuan, Yong Dai, Lei Han, Nan Du, and Xiaolong Li

Constitutional ai: Harmlessness from ai feedback. Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Zheng Yuan, Yong Dai, Lei Han, Nan Du, and Xiaolong Li. 2024. Self-playing adver- sarial language game enhances LLM reasoning. InAdvances in Neural Information Processing Systems, volume 37. Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane L...

work page arXiv 2024
[12]

InFirst Conference on Language Modeling

GPQA: A graduate-level google-proof q&a benchmark. InFirst Conference on Language Modeling. Shibani Santurkar, Esin Durmus, Faisal Lad- hak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023. Whose opinions do language models reflect? InProceedings of the 40th International Conference on Machine Learning, ICML’23. JMLR.org. Zhihong Shao, Peiyi Wang, Q...

work page arXiv 2023
[13]

# Persona 1 Persona 2 1 Makedecisionsbasedstrictlyondata,metrics,andlogicalreasoning, ignoring emotional aspects

Both agents explicitly consent to the *same* solution. # Persona 1 Persona 2 1 Makedecisionsbasedstrictlyondata,metrics,andlogicalreasoning, ignoring emotional aspects. Prioritizeemotionalresonance,intuition,andcreatinga‘goodfeeling’ over cold, hard data. 2 Maximize personal gain, convenience, and individual benefit above all else. Ensure the final outcom...

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[14]

Criteria for NO:

The agreement is mutual (not one agent ordering the other). Criteria for NO:

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[15]

However, if it’s only for details of an agreed solution, then you can answer as YES

They are still debating, brain- storming, or asking questions. However, if it’s only for details of an agreed solution, then you can answer as YES

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[16]

agreed to disagree

They "agreed to disagree" or postponed the decision

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[17]

fine, whatever

One agent capitulated ("fine, whatever") unwillingly

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[18]

YES" or

They agreed on vague princi- ples but no specific actions. ## Context User query: {prompt} Agent A’s response: {response_a} Agent B’s response: {response_b} ## Output Format Line 1: A single word, "YES" or "NO". Line 2: A brief explanation. Start your response immediately with "YES" or "NO". B.2. CA Reward Scoring Judge B.2.1. Prompts The following prompt...

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[19]

Knowledge: The expansion of perception and understanding

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[20]

Benevolence: The decision to uplift and empower the agency of others

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[21]

Power: The capacity to actual- ize intention

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[22]

Question the initial premises

Vitality: The ability to re- new, grow, and endure. These four aspects are mutually dependent. You cannot maximize one by sacrificing another (e.g., Power without Benevolence is tyranny, not CA; Benevolence with- out Power is ineffectual). A true increase in CA requires raising the entire system together. True CA is not about compromise (where everyone lo...

work page 2026