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REVIEW 5 major objections 7 minor 113 references

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

T0 review · 5 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This thesis claims that language models become better cooperators when retrained on in-game transitions selected by state features, or used as distillation teachers for RL agents, with average per-game gains around 53% and fivefold sample…

desk verdict A transparent thesis proposal that compiles two earlier papers; the theory-of-mind framing is unsupported, the headline 53% gain is inflated and oracle-dependent, but the Hanabi-text benchmark and distillation result are worth a look. read the letter →

arxiv 2506.09331 v2 pith:XXLAC5JU submitted 2025-06-11 cs.CL cs.AIcs.MA

Arjun Vaithilingam Sudhakar This is my paper
classification cs.CLcs.AIcs.MA
keywords largelanguagemodelsmulti-agentreinforcementlearningtheoryofmindtext-basedgamesHanabiknowledgedistillationsampleefficiency
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

This thesis proposes that language models can be made to cooperate and coordinate by embedding them in multi-agent reinforcement learning rather than treating them as frozen text processors. The author's first demonstration retrains a GPT-2 action recommender on in-game transitions that have been categorized by a state-feature oracle; that alone raises the average normalized score on ten Jericho text games from 20.1% to 24.0%, an average per-game relative gain of about 53% as the paper reports, and reaches the baseline's best score in about half the steps. The second demonstration converts Hanabi into a text environment, finetunes small language models on expert trajectories, and distills one of them into a Rainbow agent, producing a reported jump in performance and roughly fivefold sample efficiency. The thesis frames these results as groundwork for benchmarking whether LLMs possess theory of mind, though that framing is stated as a research goal rather than tested directly.

What carries the argument

Chapter 3's mechanism is LM-in-the-Loop: a GPT-2 model already finetuned on human demonstration data is periodically retrained on in-game transitions stored in replay buffers, with the transitions split into $D^+$ and $D^-$ by a heuristic; the decisive variant is the state-feature oracle (OC), which labels a transition as useful if the action moved the agent to a new location or increased reward, trained with a weighted cross-entropy loss. Chapter 4's mechanism is a text rendering of Hanabi plus a distillation objective: observations are templated into text, expert trajectories from an off-belief-learning agent are filtered and category-sampled into a balanced dataset, a DistilBERT teacher is finetuned on it, and a Rainbow student is trained with $L = L_{\mathrm{TD}} + \lambda \mathbb{E}_{s\sim D}\big[\sum_a \pi_{\mathrm{LM}}(a|s)\log \pi_{\mathrm{student}}(a|s)\big]$, which transfers the LM's action distribution to the RL policy and produces the reported sample-efficiency jump.

What would settle it

Re-run the LM-in-the-Loop procedure on a suite of text games whose observations contain no location-change signal and whose rewards are sparse, so the OC oracle can never fire: if the normalized score does not exceed the 19.1% uncategorized baseline, the Chapter 3 claim collapses. Separately, measure the number of environment steps a Rainbow agent needs to reach a fixed Hanabi-text score with and without the DistilBERT distillation loss; the '5x sample efficient' claim is false if the two learning curves reach the same score at the same step count.

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Extended reading notes

Core claim

The central discovery, on the thesis's own terms, is that a language model earns its place inside the cooperative learning loop in two distinct roles. As an action recommender in text games, the LM improves only when the transitions used to retrain it are selected by a heuristic: labeling a transition as useful when the agent's location changes or reward increases (the 'OC' oracle) yields 24.0% average normalized score versus 20.1% for the frozen CALM baseline, which the paper describes as roughly a 53% average per-game improvement, and it also lets the agent reach that performance with only 10% of the human-annotated adaptation data (21.8% versus 20.1% for CALM at 100%). As a teacher in the Hanabi-text environment, a DistilBERT model finetuned on expert game trajectories is distilled into a Rainbow student policy through a combined TD-plus-distillation loss, producing a jump in score and about five times better sample efficiency than a from-scratch Rainbow. The author presents these as evidence that LM-in-the-Loop training and LM-to-RL distillation are viable mechanisms for adaptive, cooperative language agents, and as the first steps toward a benchmark for theory of mind in LLMs.

Load-bearing premise

The Chapter 3 gains ride on an oracle that marks a transition as useful only when the agent's location changes or the reward rises; if a target game does not expose those features, the improvement falls back to the 19.1% uncategorized baseline, and the thesis provides no independent check that Hanabi-text scores measure theory of mind.

Editorial extensions

If this is right

  • With state-feature categorization, the LM-in-the-Loop agent needs only a tenth of the human-annotated adaptation data to beat the full-data baseline, so in-game retraining can substitute for large human-annotation efforts.
  • Because LM-in-the-Loop reaches the baseline's best score in about half the environment steps, the same compute budget can cover more games or longer horizons.
  • Finetuned classifier LMs (BERT and DistilBERT) can act directly as Hanabi-text agents, scoring up to 23 of 25, which makes a text-based cooperative benchmark available to the NLP community.
  • Distilling the LM teacher into a Rainbow student yields about fivefold sample efficiency, so a single finetuned LM can amortize its expert knowledge across many RL runs.
  • Text-based Hanabi is proposed as a common testbed for multi-agent LM research, connecting MARL coordination metrics with language-model capabilities.

Reading between the lines

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

  • If the Chapter 3 mechanism is what produces the gain, replacing the location/reward oracle with learned intrinsic-motivation signals (curiosity or state-visit counts) would preserve the improvement, implying the benefit comes from selecting useful transitions rather than game-specific features.
  • The fivefold sample-efficiency figure likely depends on the teacher and student sharing the same two-player coordination regime; a stronger experiment would pair the distilled student with novel partners, where the teacher's expert distribution may mislead.
  • Because neither chapter measures a partner's predicted mental state directly, a direct theory-of-mind test would hold out the partner's next action and measure the LM's predictive accuracy against it, separately from the final game score.
  • Since the OC oracle is admitted to be a loose upper bound, deployment would require inferring transition usefulness without privileged state features, for example by estimating advantages from the agent's own returns.
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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

5 major / 7 minor

Summary. This manuscript is a PhD thesis/proposal from Polytechnique Montréal that combines two studies around language models in multi-agent settings. Chapter 3 (LM-in-the-Loop) extends the CALM approach by finetuning GPT-2 on in-game transitions from ten Jericho text games, comparing several transition-selection heuristics: Uncategorized Transitions (UT), State Feature Categorized (OC), Reward Trajectories (RT), and advantage-weighted variants (UTEA, UTLA). Chapter 4 introduces a text-based version of Hanabi, finetunes BERT/DistilBERT/GPT-2 variants on expert Off-Belief Learning trajectories, and distills a DistilBERT teacher into a Rainbow agent. The abstract frames the work as investigating theory of mind in LLMs through cooperative multi-agent reinforcement learning. The main reported results are a ~53% improvement in normalized score for the OC heuristic over CALM on ten Jericho games (20.1% to 24.0%) and an 'almost 5x' sample-efficiency gain from distillation in Hanabi. Chapters 5-8 contain future goals, risks, a timeline, and a concluding chapter.

Significance. The empirical material has genuine value. The authors benchmark against the external CALM baseline rather than only against themselves, report five runs with standard errors for the Chapter 3 experiments, provide ablations over several transition-selection strategies, and give extensive appendix studies for Chapter 4 (data scaling, LoRA ranks, model sizes, discard-pile information). The proposed text-based Hanabi environment is a potentially useful testbed for multi-agent language-model research. If the results hold as stated, the contribution is an incremental empirical study of in-game LM finetuning under a game-specific oracle and a distillation pipeline for text-based Hanabi. However, the headline gains are conditional on a privileged state-feature oracle, the relative improvement metric is inflated by games with tiny absolute scores, and no experiment measures theory of mind despite the title and abstract claiming that focus. The work is therefore best regarded as a set of empirical findings with an overreaching framing, not as a demonstrated advance in theory-of-mind reasoning.

major comments (5)
  1. [§3.5.1, Table 3.1] The headline '~53% improvement' is the mean of per-game relative deltas, and it is inflated by games with negligible absolute scores (Zork3: 0.3 to 0.7; Dragon: 0.1 to 0.3). The aggregate normalized score moves only from 20.1% to 24.0%, a relative improvement of 19.4%, not 53%. The caption's statement that OC 'scored the highest in all tasks' is also contradicted by the Detective row, where CALM scores 290.9 and OC scores 288.5. In addition, the Uncategorized Transitions baseline (19.1%) is below CALM (20.1%), so the only variant that beats the baseline is the one using the OC oracle. The paper should report absolute normalized gains as the primary metric and treat per-game relative changes as secondary.
  2. [§3.3.2, §3.5.1] The OC gain is entirely driven by a privileged heuristic: a transition is labeled useful when the agent's location changes or the reward increases. The text itself calls this 'a loose upperbound to in-game learning.' Without such state features, the method reverts to the UT level (19.1%), below the CALM baseline, and reward-based RT reaches only 20.7%. Because the method's general transition-selection mechanism does not beat the baseline, the central claim that LM-in-the-Loop 'enables improved performance' should be rephrased as an oracle-conditional upper-bound result. A target game that does not expose room-change or reward features cannot be expected to reproduce the reported gain.
  3. [§3.4.3] The key hyperparameters p+, k, buffer sizes, dLM, and nRL are described as game-specific, and p+ is varied over [0,1] in 0.1 increments and k over {2k, 5k, 10k, 20k} on the ten evaluation games, with no held-out selection or sensitivity analysis. This means the reported margin over CALM may partly reflect per-game tuning on the test games. The paper should report the selected per-game values and show that the gains are robust to fixed or cross-game hyperparameters.
  4. [Abstract, Chapter 1, Chapter 5, Chapter 8] The abstract and introduction claim that the work 'investigates theory of mind in LLMs through the lens of cooperative multi-agent reinforcement learning,' but no experiment in the thesis measures belief inference, intention modeling, perspective-taking, or partner-specific adaptation. The Hanabi and text-game tasks are asserted as cooperative proxies for theory of mind without validation. The research goals in §1.1 and Chapter 5 are future work, not results. The title, abstract, and introductory claims should be aligned with what is actually demonstrated: in-game finetuning of an LM action recommender and distillation of a finetuned LM into an RL agent.
  5. [§4.4.3, Figure 4.4] The claim that distillation makes the Rainbow agent 'almost 5x more sample efficient' is not defined or quantified anywhere in the text. Figure 4.4 shows no error bars, no explicit timestep markers, and no comparison protocol that would let a reader verify a 5x factor. The schedule for λt is described only as a 'scheduler' that weans the student off the teacher. The paper should define the sample-efficiency measure (e.g., area under the learning curve or steps to reach a fixed score), report multiple seeds with standard errors, and state the exact λ schedule.
minor comments (7)
  1. [§2.1.2, Eq. (2.3)] Equation (2.3) defines L_LLM using log p(A|S), but the following sentence says 'p(S|A) represents the conditional probability of predicting an action A, given the state S'; the notation should be corrected to p(A|S).
  2. [§2.2.4] The section title 'Deep Relevence Recurrent Network' contains a typo ('Relevence' should be 'Relevance').
  3. [§3.5.1] The text says 'Table A.1 compares the different methods,' but the table that appears is Table 3.1; the cross-reference should be corrected.
  4. [§4.3.2] The dataset split is described as reserving 10% for testing and then splitting the remainder into 90% train and 10% validation; please state explicitly whether the validation split is taken from the remaining 90% after the test set is removed, and report the final instance counts.
  5. [Table 3.1] The column header 'R T' in Table 3.1 should be 'RT' to match the method name 'Reward Trajectories (RT)' in §3.3.2.
  6. [§3.5.2, Table 3.2] The comparison of OC with 10% data (21.8%) to CALM with 100% (20.1%) is interesting, but the standard errors in Table 3.2 are large for several games (e.g., Inhumane 15.7 [14.7]); a paired statistical test across the ten games would strengthen the claim.
  7. [§4.4.1, Appendix B.1] The LoRA/LLaMA results in Appendix B.1 are presented without error bars or number of seeds; since the gameplay scores are around 0-1 on a 25-point scale, please report the variance before concluding that LLaMA performs poorly.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central comparisons are against external baselines (CALM and Rainbow); the OC oracle is an acknowledged upper bound, not a definitionally forced prediction.

full rationale

I walked the paper's claimed derivation chain and found no step where a prediction or first-principles result is equivalent to its input by construction. Chapter 3 compares LM-in-the-Loop against the external CALM baseline on the Jericho benchmark; the OC transition-selection heuristic is an input to training, not a term in the evaluation metric, and the paper explicitly labels it 'a loose upperbound to in-game learning' (Section 3.5.1). The reported 24.0% normalized score is an empirical outcome, not a restatement of the OC labeling rule. The game-specific hyperparameters p+ and k are tuned on the evaluation games, which raises a generalization or overfitting concern but is not circularity. Chapter 4's distillation result is measured against a Rainbow agent trained from scratch, an external baseline; the distillation loss (Eq. 4.1) encourages imitation of the teacher, but the claimed jump in performance and sample efficiency is not logically forced by that loss and is validated by the learning curves. The thesis does cite the author's own prior papers ([42], [71], [72]) and reproduces their content as chapters; these self-citations are normal for a thesis and are not load-bearing external justification. The theory-of-mind framing is not operationalized by any direct experiment, which is a construct-validity gap rather than a circular reduction. Overall, the paper is self-contained against external benchmarks, so the circularity score is low.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central quantitative claims depend on several hand-chosen thresholds and game-specific hyperparameters, and on the domain assumption that text-game cooperation mirrors theory of mind. The Hanabi-text environment is a new testbed but is not a strictly defined or released artifact.

free parameters (5)
  • p+ (probability of sampling positive transitions) = swept 0.0 to 1.0 in 0.1 increments, game-specific
    Controls the balance of D+ and D- during LM finetuning (Sections 3.3.2, 3.4.3). Chosen per game on the evaluation games.
  • k (LM finetuning frequency) = swept 2k, 5k, 10k, 20k, game-specific
    Determines how often the LM is updated with in-game transitions (Section 3.4.3). Tuned per game on the evaluation set.
  • dLM (LM training sample size) = not specified, buffer size 100K
    Number of transitions sampled for LM update (Section 3.4.3). Hyperparameter without a fixed value.
  • Trajectory filter threshold for Hanabi expert data = score > 20
    Used to filter OBL expert trajectories before LM finetuning (Section 4.3.2). Hand-chosen threshold.
  • Per-action class balance = 2200 samples per action type
    Categorical sampling to address action imbalance (Section 4.3.2). Hand-chosen, affects the LM training distribution.
assumptions (4)
  • domain assumption Cooperative performance in text games (Hanabi, Jericho) is a valid proxy for theory of mind and real-world cooperation.
    Invoked in the abstract and Chapter 1, but no direct theory-of-mind measurement is provided.
  • ad hoc to paper The OC oracle (room change or reward increase) identifies useful transitions for LM finetuning.
    Defined in Section 3.3.2 and called an 'Oracle' in Section 3.5.1; it is game-specific and cannot be applied to arbitrary games without modification.
  • ad hoc to paper Filtering expert Hanabi trajectories with score > 20 and balancing action types yields a representative expert policy distribution.
    Curation choices in Section 4.3.2; no analysis showing the filtered set is unbiased or sufficient.
  • standard math Standard background in MDP/POMDP, DQN, DRRN, and transformer language models is correct and applicable.
    Background chapter (Chapter 2) relies on textbook definitions; assumed as standard.
invented entities (1)
  • Hanabi-text environment
    purpose: A text rendering of Hanabi observations and actions so that language models can act as agents in a multi-player cooperative setting.
    Introduced in Chapter 4; built from a template over existing Hanabi game logic and a private dataset, not released, and not a physical entity.

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Cite this review

Pith. "Pith review of Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation." pith.science (2026). https://pith.science/paper/XXLAC5JU

@misc{pith2026250609331,
  author       = {Pith},
  title        = {Pith review of: Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XXLAC5JU}},
  note         = {Machine review of arXiv:2506.09331}
}
read the original abstract

Modern Large Language Models (LLMs) exhibit impressive zero-shot and few-shot generalization capabilities across complex natural language tasks, enabling their widespread use as virtual assistants for diverse applications such as translation and summarization. Despite being trained solely on large corpora of text without explicit supervision on author intent, LLMs appear to infer the underlying meaning of textual interactions. This raises a fundamental question: can LLMs model and reason about the intentions of others, i.e., do they possess a form of theory of mind? Understanding other's intentions is crucial for effective collaboration, which underpins human societal success and is essential for cooperative interactions among multiple agents, including humans and autonomous systems. In this work, we investigate the theory of mind in LLMs through the lens of cooperative multi-agent reinforcement learning (MARL), where agents learn to collaborate via repeated interactions, mirroring human social reasoning. Our approach aims to enhance artificial agent's ability to adapt and cooperate with both artificial and human partners. By leveraging LLM-based agents capable of natural language interaction, we move towards creating hybrid human-AI systems that can foster seamless collaboration, with broad implications for the future of human-artificial interaction.

Figures

Figures reproduced from arXiv: 2506.09331 by the authors.

Figure 2
Figure 2. The interaction between an agent and the environment in RL [1]. . . 7 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 2
Figure 2. The interaction between an agent and the environment in RL [1]. [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figures from the paper (7 more)
Figure 3
Figure 3. Figure 3: Sample gameplay from zork1 game in Jericho using LM for action recommendation: [PITH_FULL_IMAGE:figures/full_fig_p020_3.png]
Figure 3
Figure 3. Figure 3: Training LM-in-the-Loop post-human-annotated dataset adaptation: RL agent [PITH_FULL_IMAGE:figures/full_fig_p023_3.png]
Figure 3
Figure 3. Figure 3: We see that LM-in-the-Loop techniques only need half of the steps to achieve [PITH_FULL_IMAGE:figures/full_fig_p027_3.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p032_4.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p034_4.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p035_4.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p036_4.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.