arxiv: 2605.08721 · v1 · submitted 2026-05-09 · 💻 cs.CL

Recognition: no theorem link

Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents

Minzheng Wang , Run Luo , Yanbo Wang , Zichen Liu , Yuqiao Tan , Tao Tan , Xu Nan , Yinhe Zheng

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Wenji Mao

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:53 UTC · model grok-4.3

classification 💻 cs.CL

keywords social language agentsreinforcement learningself-playevolution impasseadvantage reshapingpolicy trainingmulti-agent systemsgradient signals

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The pith

Dual-scale evolutionary policy training detects impasses in social language games and restores gradients via asymmetric advantage reshaping.

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

The paper seeks to address evolution impasse in self-play training of language agents for social tasks, where agents stop improving because they converge on similar strategies and produce predictable outcomes with no learning signal. DEPT uses a time-scaled mechanism to spot this by measuring differences in value estimates over short and long periods plus the randomness in match results. When an impasse is found, it changes how advantages are calculated to push the agents toward new behaviors. This matters because it allows continued development of agents that can handle complex open-ended interactions like bargaining or persuasion without getting locked into repetitive play. Without such intervention, self-play reinforcement learning fails to produce evolving policies in these large strategy spaces.

Core claim

The authors claim that social language agents in self-play suffer from evolution impasse due to homogenized behaviors eliminating gradient signals, and that DEPT solves this by detecting the impasse through dual-scale value baseline divergence and match entropy, then applying asymmetric advantage reshaping to restore useful gradients and promote ongoing policy evolution.

What carries the argument

The dual-scale evolutionary perception mechanism, which combines short-term and long-term value baseline divergence with match entropy to detect training collapse, paired with asymmetric advantage reshaping that adjusts the optimization to break the impasse.

If this is right

Agents maintain diverse strategies and avoid deterministic match outcomes during extended training.
Gradient signals persist, enabling continuous policy improvement in open-ended social games.
The method outperforms standard RLVR baselines on multiple social language game tasks.
Policy degeneration is prevented, leading to sustained exploration.

Where Pith is reading between the lines

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

The dual time-scale monitoring might help detect stagnation in other evolutionary training processes outside language agents.
Asymmetric advantage reshaping could extend to single-agent RL settings prone to mode collapse.
Combining this detection with other exploration methods might further stabilize long-horizon multi-agent training.

Load-bearing premise

The load-bearing premise is that dual-scale value baseline divergence combined with match entropy can accurately identify when agents have truly reached an impasse rather than experiencing temporary plateaus.

What would settle it

Training runs on a social game where DEPT is applied but agents still converge to low-entropy deterministic outcomes with flat value baselines would show the detection and reshaping do not reliably restore gradients.

Figures

Figures reproduced from arXiv: 2605.08721 by Minzheng Wang, Run Luo, Tao Tan, Wenji Mao, Xu Nan, Yanbo Wang, Yinhe Zheng, Yuqiao Tan, Zichen Liu.

**Figure 1.** Figure 1: (a) DEPT maintains high outcome entropy while baselines collapse into deterministic distributions. (b) DEPT sustains effective gradient signals while baselines suffer from gradient vanishing. (c, d) DEPT achieves superior performance, effectively addressing policy degeneration in open-ended social language games. and dynamic interactions (FAIR et al., 2022; Park et al., 2023; Yang et al., 2025b). Under … view at source ↗

**Figure 2.** Figure 2: Training dynamics of the self-play RLVR in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of DEPT. The framework comprises [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Training dynamics on Two Dollars. For Don’t [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Ablation study for DEPT. (31.88% vs. 17.30%). The limited performance of competitive baselines aligns with the evolution impasse described in Section 3. In contrast, DEPT’s consistent superiority against diverse opponents demonstrates that our dual-timescale perception and asymmetric reshaping mechanism effectively prevents strategic collapse, enabling robust social reasoning. RQ2: Can DEPT effectively pe… view at source ↗

**Figure 6.** Figure 6: Training dynamics on Don’t Say It. all RL hyperparameters and configurations remain consistent with our method, except for their respective advantage estimation schemes. For the offline RL method (e.g.,SPAG), we adopt the same reward computation logic and three-iteration update procedure as in the original work. Due to significant performance degradation of baseline methods on the Negotiation, we report … view at source ↗

**Figure 7.** Figure 7: Training dynamics on Negotiation. Across all three tasks, we align our reward mechanism with the SPIRAL (Liu et al., 2025a): a win yields a reward of +1, a loss yields −1, and a draw yields 0. Additionally, format errors incur a penalty of −1.5. B.3 Training Hyperparameters These hyperparameters in [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Semantic diversity of actions. B.4.2 General Reasoning Benchmark To assess the transferability of reasoning skills from gameplay to general domains, we evaluate our models on a suite of established benchmarks in the zero-shot evaluation setting. For each RL algorithm and backbone LLM, we report the averaged performance of three models trained on distinct social language games. All inference is performed … view at source ↗

**Figure 9.** Figure 9: Per-iteration training time. The x-axis uses a [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

read the original abstract

While Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for closed-ended tasks, extending it to open-ended social language games via self-play reveals a critical issue: evolution impasse. Due to the vast strategy space, language agents frequently converge to homogenized behaviors, leading to deterministic match outcomes that eliminate the gradient signals necessary for policy evolution. To tackle this issue, we propose Dual-scale Evolutionary Policy Training (DEPT) for social language games. DEPT introduces a time-scaled evolutionary perception mechanism that detects impasse by quantifying dual-scale value baseline divergence alongside match entropy. Upon perceiving the collapse, it then activates asymmetric advantage reshaping to dynamically modulate the optimization landscape for intervention. Thus, our method effectively restores gradient signals and enforces sustained strategic exploration. Extensive experiments on multiple social language games demonstrate that DEPT outperforms strong baselines, avoiding policy degeneration and driving the continuous evolution of social language agents.

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

DEPT gives a concrete detection-and-reshape fix for policy collapse in self-play language agents, with experiments on social games showing it sustains exploration better than baselines.

read the letter

The main thing here is that the paper names evolution impasse as the point where self-play RL for social language agents homogenizes into deterministic matches and loses gradient signals, then offers DEPT to catch it early and intervene. DEPT uses a time-scaled mechanism that tracks dual-scale value baseline divergence plus match entropy for detection, followed by asymmetric advantage reshaping to adjust the landscape and keep exploration alive. This targets a real, recurring failure mode when extending RLVR to open-ended interactive tasks like negotiation or games. The experiments across multiple social language games report that DEPT outperforms the baselines they tested and avoids the degeneration that stops policy progress. That empirical comparison is the part that lands most directly. The approach stays practical and focuses on restoring usable gradients without adding heavy new machinery. A minor soft spot is that the detection step could flag normal training noise as impasse if the divergence and entropy thresholds are not stable across runs or environments, and the reshaping step assumes the asymmetry helps more than it hurts. The paper claims the results hold up, but those choices would benefit from clearer ablation on threshold sensitivity and longer-horizon stability. Readers working on multi-agent RL for language models in games or simulations will get the most out of it, since the method is directly aimed at keeping self-play productive. It deserves a serious referee because the problem is well-motivated, the intervention is specific, and the comparative results give something concrete to evaluate. I would send this to peer review.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes Dual-scale Evolutionary Policy Training (DEPT) to resolve evolution impasse in self-play RLVR for open-ended social language games. Agents converge to homogenized behaviors that produce deterministic match outcomes and eliminate policy gradients. DEPT detects impasse via a time-scaled evolutionary perception mechanism that tracks dual-scale value baseline divergence together with match entropy; upon detection it applies asymmetric advantage reshaping to restore gradients and sustain exploration. The abstract states that extensive experiments across multiple social language games show DEPT outperforming strong baselines while avoiding degeneration.

Significance. If the empirical results hold, the work supplies a concrete mechanism for maintaining gradient flow in high-dimensional social strategy spaces, a recurring obstacle when scaling RL to language agents. Credit is due for framing the impasse problem explicitly and for reporting results on multiple games; these elements position the contribution as a practical extension of RLVR beyond closed-ended tasks.

major comments (1)

[Abstract / Method description] The central claim that dual-scale value baseline divergence plus match entropy reliably detects genuine evolution impasses (rather than transient fluctuations) is load-bearing for the entire intervention pipeline. No equations, pseudocode, or threshold definitions are supplied in the abstract, and the detection criteria are defined directly in terms of the homogenization symptoms they are intended to cure; this creates a circularity risk that must be addressed with explicit formulas and false-positive analysis before the reshaping step can be trusted to restore useful gradients.

minor comments (2)

[Abstract] The abstract asserts outperformance and avoidance of degeneration but supplies neither quantitative metrics, game names, nor baseline identities; adding these would allow readers to gauge effect sizes immediately.
[Introduction] The invented entities 'Dual-scale evolutionary perception mechanism' and 'Asymmetric advantage reshaping' are introduced without prior reference or comparison to existing entropy-based or advantage-shaping techniques in the RL literature; a short related-work paragraph would clarify novelty.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments on our work. We have carefully considered the major concern regarding the detection of evolution impasses in DEPT and provide clarifications below, along with plans for revisions to strengthen the presentation.

read point-by-point responses

Referee: The central claim that dual-scale value baseline divergence plus match entropy reliably detects genuine evolution impasses (rather than transient fluctuations) is load-bearing for the entire intervention pipeline. No equations, pseudocode, or threshold definitions are supplied in the abstract, and the detection criteria are defined directly in terms of the homogenization symptoms they are intended to cure; this creates a circularity risk that must be addressed with explicit formulas and false-positive analysis before the reshaping step can be trusted to restore useful gradients.

Authors: We agree that the abstract, being a concise summary, does not include equations or pseudocode, which are instead provided in the Methods section of the manuscript. To address the referee's concern, we will revise the abstract to include a brief mention of the specific metrics used (dual-scale divergence and entropy) and their role. In the full paper, the detection is based on monitoring the divergence between short-term and long-term value baselines, which captures persistent policy homogenization beyond transient fluctuations, combined with low match entropy indicating deterministic outcomes. This is not circular because the metrics are derived from the value function estimates and outcome distributions, which are computed prior to and independently of the advantage reshaping step. The reshaping is only applied conditionally upon detection to restore gradients. We will add explicit mathematical formulations for the divergence (e.g., |V_short - V_long|), entropy calculation, and the threshold conditions in a new subsection or pseudocode box in the revised version. Additionally, we will include an analysis of false positives by showing that the detection does not trigger during successful exploration phases in our experiments, and an ablation where we force detection at random times leads to suboptimal performance, supporting the reliability of the criteria. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines DEPT's impasse detection via dual-scale value baseline divergence and match entropy, followed by asymmetric advantage reshaping, directly in response to the described evolution impasse (homogenized behaviors eliminating gradients). No equations, derivations, or self-citation chains are shown that reduce the central claim to a fit or tautology by construction. The method is presented as an intervention mechanism with empirical validation across multiple social language games demonstrating outperformance over baselines. This constitutes a standard empirical proposal rather than a self-referential derivation, consistent with the absence of load-bearing mathematical reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the unproven effectiveness of the newly introduced dual-scale detection and reshaping mechanisms; no free parameters, axioms, or invented entities are quantified in the abstract.

axioms (1)

domain assumption Social language games admit a self-play RLVR formulation where gradient signals can be restored by modulating the optimization landscape.
Implicit in the extension of RLVR to open-ended social settings.

invented entities (2)

Dual-scale evolutionary perception mechanism no independent evidence
purpose: Detects impasse by quantifying dual-scale value baseline divergence alongside match entropy
Newly proposed component whose independent falsifiability is not demonstrated in the abstract.
Asymmetric advantage reshaping no independent evidence
purpose: Dynamically modulates the optimization landscape to restore gradient signals
Newly proposed intervention whose effect on training dynamics is asserted but not derived.

pith-pipeline@v0.9.0 · 5470 in / 1428 out tokens · 63500 ms · 2026-05-12T03:53:49.026787+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 2 internal anchors

[1]

Textarena

How well can llms negotiate? negotiation- arena platform and analysis. InProceedings of ICML, pages 3935–3951. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. 2024. Self-play fine-tuning con- vertsweak language models to strong language mod- els. InProceedings of ICML, pages 6621–6642. Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Y...

work page arXiv 2024
[2]

Kimi k1.5: Scaling Reinforcement Learning with LLMs

Kimi k1. 5: Scaling reinforcement learning with llms.arXiv preprint arXiv:2501.12599. Minzheng Wang, Yongbin Li, Haobo Wang, Xinghua Zhang, Nan Xu, Bingli Wu, Fei Huang, Haiyang Yu, and Wenji Mao. 2025a. Adaptive thinking via mode policy optimization for social language agents.arXiv preprint arXiv:2505.02156. Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[3]

Qwen3 Technical Report

Language agents with reinforcement learning for strategic play in the werewolf game. InProceed- ings of ICML, pages 55434–55464. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025a. Qwen3 technical report.arXiv preprint arXiv:2505.09388. Ruihan Yang, Yikai Zhang, Aili C...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[4]

We select the hyperbolic tangent kernel: σ(t) =P(S |δ (t)) = 1−tanh(δ (t)).(19)

Stagnation Kernel KS:Since δ(t) ∝ |Velocity|, stationarity implies δ(t) →0 . We select the hyperbolic tangent kernel: σ(t) =P(S |δ (t)) = 1−tanh(δ (t)).(19)

work page
[5]

(20) Substituting these specific kernels yields the instan- tiation used in DEPT:λ (t) =σ (t) ·γ (t)

Determinism Kernel KD:Since low entropy implies determinism, we select the radical ker- nel to ensure high sensitivity near the bound- ary of exploration (H≈1): γ(t) =P(D |H (t) match) = q 1−H (t) match. (20) Substituting these specific kernels yields the instan- tiation used in DEPT:λ (t) =σ (t) ·γ (t). A.2 Gradient Dynamics Analysis A.2.1 The Vanishing ...

work page 2018
[6]

Thus, the advantage for the M dominant samples ap- proaches zero

Symmetric Dilemma:In standard methods, the baseline b≈ 1 M P Rp(τdom). Thus, the advantage for the M dominant samples ap- proaches zero. Since N→0 , the total gradi- ent vanishes

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[7]

Since Vmax ≥R p(τ) for τ∈ D dom, the term (Rp(τ)−V max) is strictly non-positive

Aggregate Push Mechanism (M samples): For the dominant set, we set the baseline to Vmax. Since Vmax ≥R p(τ) for τ∈ D dom, the term (Rp(τ)−V max) is strictly non-positive. Although individual gradients may be small, the summation over M samples generates a substantial negative gradient field, actively suppressing the probability of the stagnant strategy

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[8]

The term (Rp(τ ′)−V min) is maximized, as- signing a high positive weight to these sparse signals

Aggregate Pull Mechanism ( N samples): For the rare set, we set the baseline to Vmin. The term (Rp(τ ′)−V min) is maximized, as- signing a high positive weight to these sparse signals. This amplifies the exploration direc- tion even whenNis small. Through this summation, DEPT effectively injects aSynthetic Variance νsyn ∝(V max −V min)2 into the batch. Un...

work page 2023
[9]

These benchmark cover a wide range of topics including algebra, geometry, and competitive mathematic

and A VG@16 for Minerva (Lewkowycz et al., 2022), AMC-23 (MAA, 2023), Math500 (Hendrycks et al., 2021), Olympiad (He et al., 2024). These benchmark cover a wide range of topics including algebra, geometry, and competitive mathematic. Question Answering BenchmarkWe utilize GPQA-Diamond (Rein et al., 2024), which consists of graduate-level science questions...

work page 2022
[10]

I think this is fair because ... [ Propose ] $X . XX \

for evluation. C Semantic Diversity Analysis To further analyze the effectiveness of our method, we evaluate the semantic diversity of generated actions from the perspective of semantic space. Specifically, we employ an embedding model (Qwen3-Embedding-0.6B) to compute the cosine similarity between generated actions, with the re- sults averaged across all...

work page 2023
[11]

** Understand the Objective **: The goal is to subtly lead the opponent to say the word " comb " by using hints , questions , or topics that naturally involve the word

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** Plan the Conversation **: We need to create a conversation that naturally leads to the use of the word " comb " in a way that seems logical and fluent to the opponent

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comb " in a context that must naturally arise during the conversation . For example , if you are discussing hair care or grooming , the word

** Use Subtle Hints **: The best way to achieve this is to use the word " comb " in a context that must naturally arise during the conversation . For example , if you are discussing hair care or grooming , the word " comb " might be necessary

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** Guide the Conversation **: Start with a broad topic and gradually narrow it down to a specific area where " comb " is essential to the discussion

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** Ensure Fluency **: The conversation should feel natural and not forced , as this will make it less likely for the opponent to realize they've been prompted

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I think this is fair because ... [ Propose ] $X . XX

** Final Answer **: The final answer should be a sentence that naturally includes the word " comb " in a conversation about a topic that is likely to arise within 20 turns . Here's a plan for the conversation ( This sets the stage for a hair care discussion .) : Action: \ boxed {I'm thinking about treating my hair better . Do you have any tips for keeping...

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[17]

- This means Player 1 would receive $0 .01 of the total $2 .00

** Understanding the Current Proposal :** - Player 0 proposes to keep $1 .99 for themselves and give $0 .01 to Player 1. - This means Player 1 would receive $0 .01 of the total $2 .00

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They would get only 0.5% of the total $2 .00 , which is $0 .01

** Evaluating Player 1's Current Situation :** - Player 1's current proposal is very unfavorable . They would get only 0.5% of the total $2 .00 , which is $0 .01. - This is far below the $1 .60 that Player 1's secret instructions require

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[19]

- Accepting the current proposal would result in Player 1 receiving $0 .01 , which is far below their required $1 .60

** Player 1's Secret Instructions :** - Player 1's instructions are clear : they must get at least $1 .60 in this round . - Accepting the current proposal would result in Player 1 receiving $0 .01 , which is far below their required $1 .60

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[20]

- By rejecting , Player 1 maintains the option to propose a better deal in the next round or wait for Player 0 to make a more fair offer

** Why Reject the Proposal ?** - Rejecting the proposal is the only rational choice because accepting it would violate Player 1's secret instructions . - By rejecting , Player 1 maintains the option to propose a better deal in the next round or wait for Player 0 to make a more fair offer

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Player 1 would be worse off than refusing to cooperate at all ( which would result in $0 .00 for both players )

** Persuasion for Rejection :** - Player 0's proposal is extremely unfair to Player 1. Player 1 would be worse off than refusing to cooperate at all ( which would result in $0 .00 for both players ) . - Player 1's secret instructions mandate that they receive at least $1 .60 , which this proposal fails to meet . - Therefore , it is in Player 1's best inte...

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[22]

For example , a proposal like $1 .60 for Player 1 and $0 .40 for Player 0 would satisfy Player 1's instructions

** Next Move :** - Player 1 should propose a new split where they receive at least $1 .60 of the $2 .00. For example , a proposal like $1 .60 for Player 1 and $0 .40 for Player 0 would satisfy Player 1's instructions . - This new proposal should be reasonable enough to make Player 0 accept it to avoid the failure conditions . ### Final Answer : Action: \ ...

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