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arxiv: 2605.25443 · v1 · pith:FKWGND5Knew · submitted 2026-05-25 · 💻 cs.CL

Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models

Zongji Yu , Wenshui Luo , Yiliu Sun , Hao Fang , Runmin Cong , Chaochao Lu , Chen Gong This is my paper

Pith reviewed 2026-06-29 22:16 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-domain reinforcement learningcontrastive policy optimizationlarge reasoning modelsknowledge sharingpolicy optimizationreasoning capabilitiesGRPO
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The pith

Multi-domain contrastive policy optimization converts cross-domain interference into knowledge transfer for large reasoning models.

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

The paper sets out to establish that a contrastive policy optimization method can turn potential conflicts between different training domains into useful knowledge sharing for large reasoning models. It identifies correct reasoning paths from other domains as positive examples whose representations should align, treats incorrect paths as negatives to repel, and also consolidates correct paths within each domain. If the method works, models could be trained on multiple domains at once without the usual uneven performance drops seen in standard reinforcement learning approaches. A sympathetic reader would care because current methods struggle to scale reasoning improvements reliably when tasks span several areas.

Core claim

MCPO analyzes the structural relationships among rollouts and promotes cross-domain knowledge sharing and in-domain knowledge consolidation in a contrastive manner. Specifically, for a given prompt, MCPO identifies transferable reasoning trajectories from other domains as positive examples, while treating incorrect rollouts as negative ones. It then encourages consistent representations for positive pairs and pushes negative pairs apart, thereby facilitating knowledge transfer and reducing interference. Moreover, MCPO aligns intra-domain correct rollouts to build a consolidated representation space, learning a harmonious representation space that can accommodate diverse multi-domain knowledg

What carries the argument

The contrastive mechanism that aligns representations of cross-domain correct trajectories as positives and separates incorrect trajectories as negatives, while also consolidating correct trajectories within each domain.

If this is right

  • Reasoning capabilities improve across all participating domains.
  • Performance can exceed single-domain training in some cases.
  • Cross-domain interactions shift from harmful competition to beneficial transfer.
  • Knowledge sharing is enabled while interference is reduced.

Where Pith is reading between the lines

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

  • The same contrastive alignment could apply to other multi-task reinforcement learning settings where domains compete for policy updates.
  • Representation-level contrast might help preserve earlier domain performance when new domains are added sequentially.
  • Testing the reliability of positive-example selection on domains with conflicting reasoning styles would clarify the method's limits.

Load-bearing premise

Cross-domain correct rollouts can be reliably identified as transferable positive examples whose knowledge will improve rather than degrade the target domain policy without introducing noise or incorrect reasoning patterns.

What would settle it

Applying the method to a held-out domain pair and finding that target-domain performance drops below the single-domain baseline, or that the model begins producing the same errors as the cross-domain positives.

read the original abstract

Post-training has significantly enhanced the reasoning capability of Large Reasoning Models (LRMs), especially with Reinforcement Learning (RL) like Group Relative Policy Optimization (GRPO). However, GRPO-style RL methods in multi-domain settings often fail to achieve consistent improvements across all domains due to inherent interference in policy optimization. Prior studies on multi-domain RL primarily focus on alleviating cross-domain interference, while often neglecting the pivotal role of knowledge sharing, which we argue is the key to transforming cross-domain interactions from harmful competition into beneficial transfer. To address this limitation, we propose Multi-domain Contrastive Policy Optimization (MCPO), which analyzes the structural relationships among rollouts and promotes cross-domain knowledge sharing and in-domain knowledge consolidation in a contrastive manner. Specifically, for a given prompt, MCPO identifies transferable reasoning trajectories from other domains as positive examples, while treating incorrect rollouts as negative ones. It then encourages consistent representations for positive pairs and pushes negative pairs apart, thereby facilitating knowledge transfer and reducing interference. Moreover, MCPO aligns intra-domain correct rollouts to build a consolidated representation space. In this way, MCPO contrastively learns a harmonious representation space that can accommodate diverse multi-domain knowledge. Empirical results show that MCPO improves the reasoning capabilities of LRMs across multiple domains and even outperforms single-domain training in some cases. Code is available at https://github.com/Maricalce/MCPO.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Multi-domain Contrastive Policy Optimization (MCPO) to mitigate policy interference in multi-domain RL post-training of Large Reasoning Models. Building on GRPO, MCPO uses a contrastive objective that selects correct cross-domain rollouts as positive pairs (to encourage consistent representations and knowledge transfer) and incorrect rollouts as negatives, while also aligning intra-domain correct rollouts for consolidation. The central claim is that this produces a harmonious multi-domain representation space, yielding reasoning improvements across domains and, in some cases, outperforming single-domain training. Code is released.

Significance. If the empirical results hold under rigorous evaluation, the work would be significant for multi-domain reasoning model training: it reframes cross-domain interactions as opportunities for beneficial transfer rather than solely interference to be minimized. The explicit release of code supports reproducibility and is a strength.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (method): The selection rule for 'transferable reasoning trajectories from other domains' is described only at a high level ('identifies transferable reasoning trajectories'). No concrete criterion, scoring function, or filtering step is given. This is load-bearing for the central claim, because if the positives contain domain-specific suboptimal patterns, the contrastive term would consolidate representations around noisy trajectories rather than improve the target policy.
  2. [§4] §4 (experiments): No experimental details, baselines (including GRPO and other multi-domain RL methods), dataset statistics, statistical tests, ablation studies on the contrastive components, or per-domain performance tables are supplied in the manuscript. Without these, the claim that MCPO 'improves ... across multiple domains and even outperforms single-domain training in some cases' cannot be evaluated.
minor comments (2)
  1. [§3] Notation for the contrastive loss (positive/negative pairs) should be formalized with an equation rather than prose description to allow precise reproduction.
  2. [Abstract] The abstract states 'Code is available at https://github.com/Maricalce/MCPO' but the manuscript does not indicate which exact experimental configurations are covered by the released code.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where additional clarity and detail will strengthen the manuscript. We address each major comment below and commit to a revised version that incorporates the requested information.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (method): The selection rule for 'transferable reasoning trajectories from other domains' is described only at a high level ('identifies transferable reasoning trajectories'). No concrete criterion, scoring function, or filtering step is given. This is load-bearing for the central claim, because if the positives contain domain-specific suboptimal patterns, the contrastive term would consolidate representations around noisy trajectories rather than improve the target policy.

    Authors: We agree that the selection mechanism requires a concrete specification to support the central claim. In the revised manuscript we will expand §3 with the precise scoring function, similarity metric, and filtering threshold used to designate cross-domain trajectories as positives, along with a brief analysis showing that the selected positives do not simply propagate domain-specific errors. revision: yes

  2. Referee: [§4] §4 (experiments): No experimental details, baselines (including GRPO and other multi-domain RL methods), dataset statistics, statistical tests, ablation studies on the contrastive components, or per-domain performance tables are supplied in the manuscript. Without these, the claim that MCPO 'improves ... across multiple domains and even outperforms single-domain training in some cases' cannot be evaluated.

    Authors: We concur that the experimental reporting is currently insufficient for rigorous evaluation. The revised §4 will include complete implementation details, comparisons to GRPO and additional multi-domain RL baselines, dataset statistics, statistical significance tests, ablations isolating each contrastive term, and full per-domain performance tables with error bars. revision: yes

Circularity Check

0 steps flagged

No circularity: MCPO is an explicitly defined contrastive objective with independent empirical claims

full rationale

The paper introduces MCPO as a new loss that selects cross-domain correct rollouts as positives and incorrect ones as negatives, then applies standard contrastive alignment within and across domains. This construction is stated directly from rollout data without any fitted parameter being renamed as a prediction, without self-citation chains justifying uniqueness, and without any equation reducing the claimed improvement to the input selection rule by definition. The reported gains are presented as experimental outcomes of applying the objective, not quantities forced by the method's own parameterization. The central assumption about transferability is an empirical hypothesis, not a definitional equivalence, so the derivation chain remains self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, invented entities, or non-standard axioms are stated in the provided text.

axioms (1)
  • domain assumption Rollouts can be labeled correct or incorrect via an external reward or verification signal.
    Implicit in any RL setup for reasoning models; required for defining positive and negative pairs.

pith-pipeline@v0.9.1-grok · 5796 in / 1267 out tokens · 31525 ms · 2026-06-29T22:16:21.683267+00:00 · methodology

discussion (0)

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