Actor–Refiner Collaboration

• Actor–Refiner Collaboration is a multi-agent paradigm where an Actor generates task solutions and a Refiner iteratively refines them through actionable, context-aware feedback.
• It leverages reinforcement and preference-based learning to structure feedback loops, ensuring systematic improvements through process- and outcome-level evaluations.
• Empirical outcomes show significant performance gains in areas like 3D modeling and multi-hop reasoning compared to traditional single-agent methods.

Actor-Refiner Collaboration is a multi-agent paradigm in which an Actor agent generates solutions to a task and a Refiner (or Critic) agent iteratively analyzes, diagnoses, and refines the Actor’s output. This collaborative structure is analytically and empirically validated in domains including multi-hop reasoning, multi-agent language modeling, and agent-augmented 3D modeling. Key formulations instantiate both reinforcement learning (e.g., Actor-Critic, process- and outcome-level rewards) and preference-based learning for constructive feedback. The approach yields systematic improvements in solution accuracy, robustness, and efficiency compared to single-agent or emergent collaboration baselines.

1. Principal Actor and Refiner Roles

The Actor agent executes the primary task, generating either code, reasoning chains, or textual responses. For instance, in the Planner–Actor–Critic framework for 3D modeling, the Actor synthesizes modeling commands from structured subtasks and the current scene state, supporting idempotent and contextualized code generation (Gao et al., 8 Jan 2026). In Search-R2, the Actor produces reasoning trajectories interleaved with external search calls (He et al., 3 Feb 2026). In ACC-Collab, the Actor generates candidate answers in a multi-turn debate (Estornell et al., 2024).

The Refiner (Critic) agent analyzes the Actor’s output with respect to explicit or learned success criteria. In 3D modeling, the Refiner evaluates both execution metadata and rendered screenshots against planner-defined constraints, issuing fine-grained JSON critiques and actionable parametric updates. Search-R2’s Meta-Refiner employs a hybrid of a discriminator (for trajectory-level coherence) and a trimmer (for pinpointing and repairing local flaws). ACC-Collab’s critic provides constructive feedback during multi-turn debate, steering the Actor toward optimal responses.

A persistent theme is that Refiner feedback is both actionable and granular, facilitating progressive refinement rather than single-pass verdicts. Extensions over single-prompt or emergent agent systems include context-aware critique, structured supervision signals, and targeted repair mechanisms.

2. Iterative Actor–Refiner Loops and Algorithms

Actor–Refiner collaboration is characterized by an iterative loop in which the Actor produces an initial output and the Refiner performs diagnostic analysis, returning suggestions for adjustment or indicating acceptance. This feedback can take the form of parameter updates (3D modeling), cut-and-regenerate instructions (search reasoning), or debate guidance (multi-agent LLMs).

An exemplar workflow in 3D modeling involves the Planner generating a sequence of subtasks; the Actor executes each, with the Refiner critiquing the result and amending subsequent plans (Gao et al., 8 Jan 2026). Search-R2 formalizes the cut-and-regenerate mechanism: the Meta-Refiner’s discriminator evaluates the overall trajectory, and if coherence falls below threshold, the trimmer identifies the earliest error point, allowing for selective prefix preservation and suffix regeneration (He et al., 3 Feb 2026). In ACC-Collab, multiple rounds of debate leverage the critic’s feedback to improve actor-generated answers over a trajectory of exchanges (Estornell et al., 2024).

Algorithmic implementations involve tracking execution summaries, encoding feedback in structured formats, and conditionally updating the next set of actions or responses. Stopping criteria typically hinge on either scriptable completion conditions or a configurable iteration cap.

3. Mathematical Formulations and Optimization Objectives

Actor–Refiner frameworks are underpinned by formal optimization criteria. In 3D modeling, the Refiner minimizes a hybrid loss composed of mean squared error to geometric targets and a reinforcement learning–style reward for satisfying success criteria. The Actor is modeled as a policy $\pi_\theta$, updated via policy gradients with the Refiner’s feedback as rewards. The Planner’s post-refinement task list is updated according to a coordination equation representing the incorporation of suggestions.

Search-R2 defines a smoothed mixture policy: $$\pi_{\mathrm{mix}}(y|x) = (1-\gamma)\cdot\pi_{\mathrm{actor}}(y|x) + \gamma\cdot\pi_{\mathrm{refiner}}(y|x)$$ where $\gamma$ is the intervention volume. Rewards are hybrid, combining sparse final correctness with a process reward reflecting the usefulness of search evidence, and the overall learning objective uses a Group Relative Policy Optimization (GRPO) scheme. Theoretical analysis shows that selective correction by the Refiner can strictly improve performance under adequate precision and trimming skill (He et al., 3 Feb 2026).

ACC-Collab casts the multi-agent learning process as an MDP, with the actor optimizing expected sum of partial-trajectory rewards. Policy optimization employs Direct Preference Optimization (DPO) on preference pairs generated via guided-debate, stabilized by a reference policy and regularization terms (Estornell et al., 2024).

4. Implementation Practices and Feedback Encoding

Actor–Refiner systems incorporate explicit mechanisms for encoding and transmitting feedback, ensuring actionable guidance and synchronization across agents and tasks. In 3D modeling, Refiner critiques are emitted as JSON objects enumerating the satisfaction per criterion, quantitative assessment, and parametric suggestions. This feedback is appended to the planner’s revision context, supporting persistent and context-aware plan updates. Real-time synchronization is achieved via messaging protocols (e.g., WebSocket), enabling seamless transmission of screenshots, metadata, and feedback between agents and front-end visualization tools.

Search-R2’s Meta-Refiner operates via probabilistic policies for trajectory acceptance/rejection and flaw localization, modulating the intervention rate via a threshold parameter. Regeneration is performed with contextual input, preserving valid output prefixes while retriggering the Actor for correction.

ACC-Collab uses LoRA adapters to specialize actor and critic agents from a common LLM backbone. Data pipelines collect guided-debate preference pairs, supporting efficient construction of strong turn-level supervision signals. Alternating DPO-based updates for actor and critic maximize reward-driven collaboration.

5. Comparative Experimental Outcomes

Empirical evaluation demonstrates that Actor–Refiner collaboration delivers marked improvements in target domain metrics. In 3D modeling, the full Planner–Actor–Critic system achieves geometric and vertex count error reductions of 67–80% relative to single-prompt baselines, with human-rated visual quality improved by a full Likert point (e.g., Q_overall increases from 2.7 to 4.2 out of 5), and task completion rates rising from 40% to 93% in five iterations (Gao et al., 8 Jan 2026).

Search-R2 achieves +5.2 to +11.4 EM accuracy point gains over prior RAG and RL baselines across general and multi-hop QA datasets; the incremental addition of meta-refiner, process reward, and joint optimization lifts average EM accordingly. Revision sensitivity studies show most gains realized after a single cut-and-regenerate pass, with limited additional benefit for further iterations. Efficiency is maintained with only ≈5% overhead, and expert judge ratings confirm superior evidence grounding and coherence (He et al., 3 Feb 2026).

ACC-Collab’s learned debate models yield improvements of 5–10 percentage points in QA accuracy over debate-only or SFT baselines. Ablations confirm the particular utility of jointly trained actor–critic pairs, with turn-level structured preference learning yielding both stable and superior performance trajectories (Estornell et al., 2024).

6. Theoretical Guarantees and Insights

Theoretical analyses confirm that precision in selection and skill in localized repair are fundamental to strict improvements under Actor–Refiner protocols. In Search-R2, formal decomposition attributes the expected reward gain $\Delta J$ to covariances in acceptance precision and trimming skill, regulated by the intervention volume. Sufficient precision and trimming guarantee improvement over direct actor sampling, with policy optimization further enhancing adaptive intervention rates.

A notable empirical insight is that explicit training for constructive disagreement in critics yields more informative and actionably critical feedback than emergent, inference-only debate. Preference pairs generated by guided interventions foster rich, tunable reward signals for per-turn updates. Partial-trajectory rewards refocus optimization from only final correctness to incremental progress, distributing credit across trajectories.

7. Applications, Extensions, and Best Practices

Actor–Refiner collaboration generalizes beyond the evaluated domains to any sequential or multi-stage reasoning, generation, or planning task. Applications span agent-augmented creative workflows (3D design), information-intensive multi-hop QA, and collaborative LLM dialogue. Best practices include minimizing preferential thresholds to enhance training signal diversity, validating alternation rounds for overfitting detection, and regularizing updates for policy stability. Structured feedback encoding, real-time synchronization, and explicit role-specialization are recurrently effective strategies.

A plausible implication is that Actor–Refiner schemas can serve as a foundation for scalable, human-in-the-loop, or fully autonomous multi-agent systems that target high-fidelity reasoning, robust solution generation, and continual improvement across interactive, open-domain tasks.