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REVIEW 4 major objections 5 minor 45 references

GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper proposes GTDE, a training paradigm in which each agent learns from a small, self-chosen group of other agents, and reports that it beats both the full-centralized and fully-decentralized baselines in large-scale multi-agent tasks.

desk verdict GTDE's adaptive grouping is a plausible new idea, but the paper's CTDE baselines are too weak to support the claim that it beats centralized training at scale. read the letter →

arxiv 2501.10367 v1 pith:2WNRTXGE submitted 2024-12-12 cs.MA cs.AI

Mengxian Li , Qi Wang , Yongjun Xu This is my paper · ORCID
classification cs.MAcs.AI
keywords multi-agentreinforcementlearningcentralizedtrainingdecentralizedexecutionandgroupedactor-criticGumbel-Sigmoidlarge-scalecooperationadaptivegrouping
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that the two standard training schemes for multi-agent reinforcement learning—centralized training with decentralized execution (CTDE), which uses all agents' information, and decentralized training and execution (DTDE), which uses only each agent's own information—both lose effectiveness as the number of agents grows. It proposes a middle path, grouped training with decentralized execution (GTDE), in which each agent selects a small set of other agents to share information with during training, and the set is chosen from its own observation history rather than from global knowledge. The authors support the proposal with experiments showing a 382% average total-reward gain over the DTDE baseline IAC in a 495-agent cooperative gathering scenario, a 100% win rate against both CTDE and DTDE representatives in a 64-agent competitive battle, and win rates on a 20-agent StarCraft benchmark that match or exceed CTDE. If correct, GTDE would make actor-critic training feasible at scales where a centralized critic is untrainable.

What carries the argument

The central object is the directed graph $G^t=(V^t,E^t)$ whose edge $\langle v_i, v_j\rangle$ means agent $i$ uses agent $j$'s information at step $t$; the group $g(v_i)$ of an agent is its set of out-neighbors. The argument is carried by the adaptive grouping module, a network that maps each agent's observation history $\tau_i$ to an $n$-variate Bernoulli distribution over possible links and samples it with Gumbel-Sigmoid to preserve differentiability, and by the group information aggregation module, which merges the histories of linked agents using either matrix multiplication with the adjacency matrix or graph attention. Together they make GTDE a spectrum that contains the prior paradigms as extremes: CTDE is recovered when all agents are pairwise linked (the complete graph), and DTDE is recovered when each agent is linked only to itself (the self-loop graph). The critic is trained on the aggregated group history $\tau'_{g(v_i)}$, while the actor uses only $\tau_i$.

What would settle it

Run GTDE on the two-button task described in the paper, where a reward is obtained only when two agents separated by a large distance press two buttons at the same time; if the two relevant agents fail to learn a link and performance stays at DTDE levels, the grouping module's assumption of inferring links from observation history alone would be falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that the information an agent needs for value estimation can be represented as a directed graph of links to other agents, and that training on the local subgraph—not the complete graph (CTDE) and not the self-loop graph (DTDE)—is both sufficient and more scalable. GTDE learns these links end-to-end: an adaptive grouping network outputs, for each agent and each other agent, a Bernoulli probability of linkage based on the agent's observation history; Gumbel-Sigmoid sampling converts this into a differentiable adjacency matrix. A group information aggregation module then combines the observation histories of linked agents, either by matrix multiplication or by masked graph attention, and the actor's value function is trained on the aggregated group trajectory. Execution remains fully decentralized because at action time each policy sees only its own observation. The paper reports that this single framework improves on both paradigms across three benchmarks, and that ablations with fixed or random grouping confirm the learned links, not the aggregation mechanism, drive the gains.

Load-bearing premise

The load-bearing premise is that an agent's own observation history is enough to infer which other agents it should coordinate with; if the relevant collaborators are far away and invisible in local observations, the learned groups become arbitrary and the reported gains would disappear, and the paper provides only empirical evidence rather than a theoretical guarantee for this step.

Editorial extensions

If this is right

  • Training a critic no longer requires input that grows with the number of agents: the value input is the aggregated group trajectory, so the framework can be extended to populations far beyond 495 agents without changing network architecture.
  • The framework can be combined with communication-based methods: the learned adjacency matrix gives a natural routing protocol for sharing information, an avenue the paper notes as complementary to GTDE.
  • In the tested scenarios, GTDE uses roughly five times less input information per agent than CTDE, which translates directly into lower communication and memory costs in deployment.
  • The adaptive grouping mechanism doubles as a discoverability tool: the links it learns can be interpreted as task decompositions, as in the Battle scenario where distant agents link to form a besieging group.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's two-button counterexample suggests a boundary condition the authors do not test: GTDE may fail when the agents that need to coordinate are mutually invisible, so a natural next experiment is to stress-test the grouping module on long-range coordination tasks and decide whether a small amount of global hint at training time is needed.
  • Because the adjacency matrix has fixed size $n \times n$ and new agents can only link to existing ones, the framework as presented is not open to population growth; a forward-looking extension would replace the matrix with a learned neighbor-querying function or a graph generative model.
  • The paper does not ablate the two aggregation methods (matrix multiplication versus graph attention), so a reader can infer that the large gains come from the adaptive grouping module rather than from the aggregation backbone; a direct comparison of the two aggregators under identical grouping would test whether attention weights add value.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes GTDE (Grouped Training with Decentralized Execution), a training-time grouping paradigm for multi-agent actor-critic methods. The key idea is to let each agent, during training, condition its value function on a dynamically selected subset of other agents' observation histories (a 'group') rather than on individual or joint observations. Grouping is learned end-to-end via a Gumbel-Sigmoid reparameterization of an adjacency matrix, with group information aggregated by matrix multiplication or by graph attention. The authors evaluate GTDE against MAPPO/IPPO on SMACv2 20v20, against MAAC/IAC on a 64-agent Battle environment, and against IAC on a 495-agent Gather environment, reporting improvements and a 100% win rate against the two baselines in Battle.

Significance. If fully supported, the GTDE idea would be a useful contribution to the large-scale MARL literature, offering a way to retain some inter-agent coordination during training without the costly joint observation. The paper provides code, ablations (GTDE-F, GTDE-U, GTDE-A), and reports the average number of agents used in the critic, which indicates a substantial reduction in input information. However, the headline claim that GTDE outperforms CTDE as the number of agents grows is not convincingly established by the current experiments, because the CTDE baselines in the large-scale scenarios are either absent or implemented in an unfavorable way, and the SMACv2 advantage over MAPPO is within one standard deviation on two of three maps. The contribution is therefore promising but needs additional empirical support.

major comments (4)
  1. [Section 5, Table 1, Fig. 4] The claim that GTDE outperforms CTDE at scale is not supported by the experiments on Battle and Gather. The only CTDE baseline in these scenarios is MAAC with a flattened joint observation, and the paper itself states that MAAC's input dimension of (13×13×5+32)×64 makes it perform worse than IAC. This is an implementation artifact of a particular CTDE design rather than a fundamental property of CTDE. A proper CTDE baseline such as MAPPO with a state-based critic, MAAC with entity-wise attention, or a value-factorization method like QMIX would not suffer from this failure mode. Consequently, the 100% win rate against MAAC in Battle cannot be interpreted as a win against CTDE in general.
  2. [Section 5, Gather, Fig. 6] The reported 382% average total-reward improvement is computed against IAC, a DTDE baseline, and no CTDE baseline is trained in this scenario. The statement in the conclusion that 'CTDE becomes untrainable due to the high input dimensionality' is a conclusion about the particular MAAC implementation, not an inherent limitation of CTDE. The paper should either run a state-based MAPPO or another scalable CTDE method in Gather, or explicitly qualify the claim as a comparison against DTDE only.
  3. [Table 1, Battle column] The table is difficult to interpret and appears to contradict the text. The text states that 'GTDE defeated them with a 100% win rate' against IAC and MAAC, while Table 1 lists GTDE's Battle value as 52.3(2.8) and CTDE/DTDE as 100.0(0.0). If the entries in the Battle column are the win rates of GTDE against the algorithm in the corresponding row, the caption must say so explicitly; if they are the win rates of the algorithms themselves, the 52.3 value for GTDE needs explanation. As it stands, the table and the text cannot both be correct, undermining the empirical basis of the Battle claim.
  4. [Section 4, Adaptive Grouping, and Table 1 ablations] The paper does not provide evidence that the adaptive grouping module learns meaningful, non-trivial groupings. In Battle, GTDE-U (uniform/random grouping) still achieves a 91.8% win rate, and in SMACv2 the gap between GTDE and GTDE-U is modest (53.4 vs 46.4, 48.4 vs 43.5, 38.8 vs 32.8). This suggests that a large part of the observed benefit may come from the aggregation of a subset of observations rather than from the specific learned grouping. The authors should report the learned adjacency matrices qualitatively and provide a quantitative measure of grouping quality, or temper the claim that adaptive grouping is the key component.
minor comments (5)
  1. [Section 4, Eq. (3)] The term 'group' is used for the set of agents that a given agent is linked to, but this does not define a partition of the agent set; two agents can have different groups and the 'number of groups' is therefore not well-defined. Suggest clarifying this terminology.
  2. [Appendix A] The derivation of Gumbel-Sigmoid uses two independent Gumbel noises (ϵ1 and ϵ2) but the text just says ϵ ∼ Gumbel(0,1); please specify that the two noises are independent.
  3. [Section 5, Hyperparameter] The paper does not report sensitivity to the mask dropout probability (0.1) or the Gumbel temperature; a short sensitivity analysis or a note on their choice would strengthen the paper.
  4. [Figure 5] The partial links in the Battle scenario are described in the text but the figure lacks labels or arrows that make it easy to see which agents are linked; the figure needs a clearer caption and legend.
  5. [References] The reference 'Ruan et al. 2023' appears in the bibliography but is not cited in the body of the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GTDE's reported gains are direct empirical measurements against external baselines, and the method's components are not defined in terms of the target results.

full rationale

The paper's central claims are empirical: GTDE is compared with IAC/IPPO (DTDE) and MAAC/MAPPO (CTDE) on SMACv2, Battle, and Gather, and the reported improvements (e.g., 382% total reward in Gather, win rates in Battle) are measured outcomes, not quantities derived from fitted parameters. The adaptive grouping module is trained end-to-end with Gumbel-Sigmoid sampling; this is a standard reparameterization technique and is not fitted to the benchmark results, nor is any benchmark metric used to define the grouping objective. There is no equation in the paper that reduces a prediction to an input by construction, and no fitted parameter is renamed as a prediction. The only self-citations (Wang et al. 2022; Wang, Huang, and Xu 2024) appear in the introduction as examples of MARL application areas and are not load-bearing for the GTDE derivation or the empirical evaluation. Concerns about the weakness of the CTDE baseline in Battle/Gather or the inconsistency between Table 1 and the claimed 100% win rate are substantive evidence-quality issues, but they are not circularity: the comparisons are still external measurements rather than self-referential definitions. Therefore the paper is self-contained against external benchmarks, and the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on domain assumptions about observability and communication, plus standard RL assumptions. No new physical entities are introduced.

free parameters (2)
  • Mask dropout probability = 0.1
    In Group Information Aggregation, a mask randomly drops adjacency entries with probability 0.1 to simulate communication failures and prevent all-to-all grouping; chosen by hand, not tuned.
  • Gumbel-Sigmoid temperature
    No temperature is reported; the Gumbel-Sigmoid sampling in Sec. 4 and Appendix A likely uses a fixed temperature of 1, a hand-chosen hyperparameter for gradient estimation.
assumptions (4)
  • domain assumption Each agent's observation history contains sufficient information to identify useful training links.
    The Adaptive Grouping module takes only tau_i as input (Sec. 4); no theoretical guarantee is provided.
  • domain assumption Agents can exchange observation trajectories within their group during training.
    Group Information Aggregation assumes access to trajectories of linked agents (Sec. 4); this is local, not global, but requires communication during training.
  • domain assumption Parameter sharing across agents is valid and does not harm learning.
    The paper sets theta = theta_1 = ... = theta_n (Sec. 4) without justification.
  • standard math PPO clipping and GAE are applicable in this multi-agent setting.
    Standard algorithms used as backbone (Sec. 3), treated as unproved background.

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Cite this review

Pith. "Pith review of GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic." pith.science (2026). https://pith.science/paper/2WNRTXGE

@misc{pith2026250110367,
  author       = {Pith},
  title        = {Pith review of: GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2WNRTXGE}},
  note         = {Machine review of arXiv:2501.10367}
}
read the original abstract

The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number of agents increases, the inherent limitations of these frameworks significantly degrade the performance metrics, such as win rate, total reward, etc. To reduce the influence of the increasing number of agents on the performance metrics, we propose a novel training paradigm of grouped training decentralized execution (GTDE). This framework eliminates the need for a centralized module and relies solely on local information, effectively meeting the training requirements of large-scale multi-agent systems. Specifically, we first introduce an adaptive grouping module, which divides each agent into different groups based on their observation history. To implement end-to-end training, GTDE uses Gumbel-Sigmoid for efficient point-to-point sampling on the grouping distribution while ensuring gradient backpropagation. To adapt to the uncertainty in the number of members in a group, two methods are used to implement a group information aggregation module that merges member information within the group. Empirical results show that in a cooperative environment with 495 agents, GTDE increased the total reward by an average of 382\% compared to the baseline. In a competitive environment with 64 agents, GTDE achieved a 100\% win rate against the baseline.

Figures

Figures reproduced from arXiv: 2501.10367 by the authors.

Figure 1
Figure 1. Each circle represents the observation of an agent, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of GTDE framework. The red dashed line represents gradient flow. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Test curve of average win rate. The shaded area represents the range between the minimum and maximum values [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The total reward curve of the Battle scenario(64 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Partial links in the Battle scenario [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The total reward curve of the Gather scenario(495 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.