Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

arXiv:2608.07086 · cs.LG, cs.AI · Submitted 2026-08-07 · Read on arXiv

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Introduction to the show: ident: Paper Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control".

Jane: The paper was written by Qi Zhao, Guozheng Ma, Yilun Kong, Lu Li, Haoyu Wang et al. from Tsinghua University and Nanyang Technological University and Mila - Quebec Artificial Intelligence Institute and University of Oxford.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Paper summary: Tom: We've got a fresh one from the arXiv pile, and it's all about why reinforcement learning doesn't just get better when you bolt on fancy tricks.

Jane: Right? This team from Tsinghua and friends asks a deceptively simple question: do those sample-efficiency modules actually work together?

Tom: Their answer? Usually no — and sometimes they actively fight each other.

Lu: So they built a framework called ROSER that coordinates three key pieces: model-based representation, optimization stability, and experience replay.

Meng: And the payoff is real. They report a 17.60 percent improvement over a naive stack of the same components.

Lalam: That's the big takeaway: RL behaves like a systems problem. The parts don't simply sum.

Jane: Exactly. They show that individual wins on a vanilla algorithm don't transfer when you combine them, because of things like compounded non-stationarity.

Tom: It's like making a smoothie with all the best fruits but forgetting to blend — you get chunks and a mess.

Lu: I'll take that analogy. They tested on 18 continuous-control tasks across four benchmarks, from humanoid locomotion to dexterous manipulation.

Meng: And the key insight? A stable optimization backbone is a prerequisite. Then you need stable information flow. And finally, you need to schedule your replay priorities gently.

Lalam: That's the deeper point: coordination beats competition. The whole is less than the sum of its parts unless you design the interactions.

Jane: So what does that mean for the field? We'll unpack it as we page through the paper, from the abstract to the conclusion.

Tom: Let's start where they start — with the motivation. Why sample efficiency even matters.

Page 1 of the paper: Jane: We just set the stage with the big picture. Now let's get into the actual page one.

Tom: This is the abstract and the opening of the introduction, and it hits hard: RL systems are more complex than other ML paradigms.

Jane: The reason? Many tightly coupled factors have to be designed together, not one at a time.

Lu: And they call out that most research studies components in isolation, leaving their interdependencies unexplored.

Meng: They list the usual suspects: representation learning, optimization stability, prioritized sampling, advanced exploration. All individually great, but nobody's checking if they amplify or cancel each other.

Lalam: The paper's first big question is whether naive stacking actually helps. They already spoiled the answer—it often triggers emergent challenges like compounded non-stationarity.

Tom: "Compounded non-stationarity" is a mouthful, but it's simple: the world inside the learner keeps shifting, and stacking modules makes those shifts more violent, not less.

Jane: Exactly. The agent's own data distribution changes as it learns, and each module adds another layer of moving target on top of a moving target.

Lu: They also frame why this matters: sample efficiency is a central challenge because interactions are expensive—time, compute, physical cost.

Meng: And they propose to investigate three components: model-based representation, optimization stability, and experience replay. That's the R, OS, and ER in their ROSER acronym.

Lalam: The intro ends with a teaser: their primary finding is that enhancements show task-dependency—a win in one task can be a loss in another.

Tom: That task-dependency is the thread that runs through the whole paper. It's not that a technique is bad; it's that context changes everything.

Jane: So page one plants the flag: don't study modules in a vacuum. Study them in a living, breathing RL system.

Tom: Next, the paper gets into the intellectual lineage. Let's look at what came before—the Rainbow-style aggregation work.

Page 2 of the paper: Jane: Good, we're moving past the intro now. The paper dives into background, and there's a clear history here.

Tom: Right, starting with Rainbow, which combined several DQN extensions and got state-of-the-art results on Atari.

Lu: Then Revisiting Rainbow argued for more inclusive evaluations, and Beyond the Rainbow pushed further with six modern enhancements on a desktop PC.

Meng: But here's the catch: those all worked in discrete action spaces, on value-based methods. And they treated modules like plug-ins, additive blocks.

Lalam: So the paper's contribution is to move that systematic lens to actor-critic methods in continuous control—and to actually inspect the interactions, not just assume they add up.

Tom: They pick three representative techniques. For representation, it's MR.Q, which learns state and state-action embeddings with auxiliary tasks—reward prediction, dynamics, terminal state.

Jane: For optimization stability, they pick SimBa, a residual network architecture with layer norm that resists plasticity loss and keeps networks adaptable.

Lu: And for experience replay, they pick ReLo, which prioritizes samples by "reducible loss"—how much a transition could actually still teach the model.

Meng: That's a clever contrast: MR.Q is model-based, SimBa is architectural, ReLo is a sampling strategy. Three very different levers.

Lalam: And they're all proven individually. The question is what happens when they share the same agent.

Tom: Exactly. The background also stresses that these choices aren't arbitrary; they did a comparative analysis of replay methods and settled on ReLo.

Jane: So now we have the cast of characters. Next, the investigation section shows us the drama—who helps and who hurts whom.

Page 3 of the paper: Tom: Time to open the investigation. They set up a huge testbed: 18 tasks, nine locomotion and nine manipulation, across DMC, HumanoidBench, MyoSuite, and ManiSkill2.

Jane: They threw SAC in as the base, used a fixed hyperparameter set, and ran each configuration with eight seeds. Then they reported IQM with confidence intervals.

Lu: That's a serious empirical scaffold. Not cherry-picked tasks, not a single gym suite.

Meng: Now the first result looks at optimization stability—the SimBa backbone. They compared SAC, SAC+R, SAC+ER, each with and without OS.

Lalam: And the pattern is crystal clear: turning on OS gives a consistent boost in every single configuration. It's like adding a solid foundation to a building.

Tom: The fascinating bit is how OS interacts with representation. Adding MR.Q to vanilla SAC actually hurts performance, especially on locomotion tasks.

Jane: But add OS on top of that, and R+OS dramatically outperforms either alone. The paper calls that super-additive, "1+1>2."

Lu: So a module that's a net negative in isolation becomes a huge positive when paired with a stable backbone.

Meng: That's the key insight: optimization stability isn't just a nice extra. It's an enabler that unlocks other modules' potential.

Lalam: The term they use is "synergistic amplification." And it makes sense—without a stable learning dynamic, a representation module just gives you another moving target to chase.

Tom: So takeaway one: a stable network backbone is essential for synergy. But there's more—they push this stability principle even further.

Jane: Right, that's where R* comes in. But first, they hit a wall with experience replay. Let's see how that plays out.

Page 4 of the paper: Tom: We're now in the thick of the investigation, and the paper just showed that OS is a backbone. But adding experience replay on top of a stabilized system? That's where things fall apart.

Jane: They integrate prioritized replay into their best combo so far, and it underperforms the version without it. A clear counteractive effect.

Lu: The paper's diagnosis: early in training, priority signals are unreliable because the representation and value estimates are still evolving. So you're sampling based on noise.

Meng: So they design U2P—Uniform-to-Prioritized replay. It starts fully uniform, then gradually ramps up the priority exponent alpha over the middle of training.

Lalam: It's a scheduling solution. It lets the system stabilize before you let the replay distribution become aggressive.

Tom: And that works. U2P reconciles ER with R* and OS, whereas naive ER breaks the synergy.

Jane: This is a clean demonstration of their third takeaway: naive stacking can be counterproductive; coordination requires scheduling.

Lu: It's not that prioritized replay is bad—it's that it's bad at the wrong time.

Meng: So the investigation yields three design principles: OS as the foundation, stable information flow for the representation, and robust coordination over aggressive individual performance.

Lalam: Next they wrap those principles into a concrete framework and run the full comparison. That's where the 17.60 percent gain over naive stack lives.

Tom: Right, so let's see ROSER in action.

Page 5 of the paper: Jane: Now we're at the experiments. They've packaged everything into ROSER—Simba blocks across the encoder, actor, and critic, plus R* for stable information flow, plus U2P replay.

Tom: They stress it's algorithm-agnostic—SAC is the primary base, but there's a DDPG version in the appendix.

Lu: The headline comparison is ROSER against vanilla SAC, single-component variants, and a naive stack that just throws all three components together.

Meng: The performance profiles in Figure 6 are a great tool—they plot what fraction of runs exceed a given normalized score. ROSER sits in the top-right corner.

Lalam: In the low score range, ROSER keeps nearly every run above the bar, while vanilla SAC and SAC+R tank. So ROSER also reduces catastrophic failures.

Tom: At the high-performance end, especially in manipulation, ROSER keeps more runs above score thresholds like 0.8. That's reliability, not just average performance.

Jane: The gap between ROSER and the naive stack is the money result: on locomotion, ROSER is clearly ahead; on manipulation, the gain is more modest but still there.

Lu: Learning curves across 18 tasks show ROSER both converges faster and ends higher, while single-component variants are wildly inconsistent—sometimes great, sometimes terrible.

Meng: So the framework works, but the paper doesn't stop there. They want to know why R* and U2P actually help—are they good on their own or only in synergy?

Lalam: That's the question that leads to the analysis section, and it's a real twist.

Page 6 of the paper: Tom: We're at the analysis now, and this is where the paper gets really sharp.

Jane: They design a clean experiment: test R* and U2P in isolation on vanilla SAC, then in the full ROSER context, on six representative tasks.

Lu: Table 1 shows the reversal beautifully. R* on vanilla SAC? Performance drops by 0.015. But inside ROSER? It jumps by 0.143.

Meng: Same for U2P: on vanilla SAC, it actually hurts by 0.085. In ROSER, it adds 0.042.

Lalam: So both modules are worthless or harmful on their own, yet valuable in a coordinated system. They're not optimizers; they're stabilizers that fix integration problems.

Tom: R* addresses joint optimization coupling—it keeps raw signals accessible to downstream networks, which matters only when the system is complex enough.

Jane: U2P acts as buffer-level regularization, mitigating compound non-stationarity that emerges from combining modules.

Lu: This is a beautiful refutation of the "customize until each part shines" approach. Parts can shine for the wrong reasons.

Meng: The conclusion then drives it home: sample efficiency is a systems problem, gains come from coherent co-design, not isolated accumulation.

Lalam: They also list honest limitations: no systematic taxonomy of which environment features favor which techniques, and their coordination mechanisms are empirically grounded, not theoretically optimal.

Tom: And they mention they only covered three components; exploration is a big missing piece for future work.

Jane: All right, so we've been through the whole arc—from isolation to synergy. Time to wrap up.

Conclusion: Tom: So let's close it out. The paper makes a single, powerful point: stop studying RL components in a vacuum.

Jane: Optimization stability is the enabler, stable information flow amplifies it, and replay prioritization needs careful scheduling—that's the recipe for ROSER.

Lu: Their empirical sweep across 18 tasks shows that the naive stack underperforms the coordinated framework, and the gap is real—17.60 percent in normalized performance.

Meng: More importantly, the component-level analysis shows why: what looks like a bad module on vanilla can be a great module in a complex system, and vice versa.

Lalam: That shifts the burden for RL research. We shouldn't just ask "does this trick work?" but "under what conditions does it synergize?"

Tom: For practitioners, that's a warning: don't just copy-paste state-of-the-art building blocks. Design their interactions.

Jane: And for the field, it's a call to treat RL as a genuine systems engineering challenge—where the whole is anything but the sum of its parts.

Lu: The limitations they list point to the open agenda: better taxonomies of task features, theory-grounded coordination, and extending to exploration and other algorithms.

Meng: We'll be watching for follow-ups that unpack those next.

Tom: For now, the paper gives us a solid framework and a sharper question to ask about every new enhancement.

Jane: Exactly. And with that, we're ready to move on to the next paper on the stack.

Tom: Thanks for listening, folks. See you on the next episode.

Qi Zhao, Guozheng Ma, Yilun Kong, Lu Li, Haoyu Wang, Zilin Wang, Tiantian Zhang, Yuxing Wang, Jian Sha, Yongzhe Chang, Xueqian Wang, Dacheng Tao

Tsinghua University · Nanyang Technological University · Mila - Quebec Artificial Intelligence Institute · University of Oxford

cs.LG, cs.AI

Submitted: 2026-08-07

Updated: 2026-08-10

Comments: 27 pages including appendix, 10 figures, 12 tables

Code: https://github.com/myohub/myosuite

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 76/100

The gist: This paper investigates whether different sample-efficiency-enhancing techniques in reinforcement learning (RL) exhibit mutual synergy or counterproductive interference when integrated into a unified

Key concepts

Compounded non-stationarity
In RL, the agent's data distribution changes as it learns. Stacking multiple modules adds more moving targets on top of this, making learning unstable. The paper shows this can cause modules that work individually to fail when combined.
Optimization stability (OS)
A stable network backbone, like the SimBa architecture with layer norm, resists plasticity loss and keeps networks adaptable. The paper finds OS is a prerequisite for synergy, enabling other modules like representation learning to work effectively.
Uniform-to-Prioritized replay (U2P)
A replay scheduling strategy that starts with uniform sampling and gradually increases prioritization. This avoids sampling based on unreliable priority signals early in training, mitigating the counteractive effect of naive prioritized replay in a coordinated system.

Terminology

Summary

This paper investigates whether different sample-efficiency-enhancing techniques in reinforcement learning (RL) exhibit mutual synergy or counterproductive interference when integrated into a unified system. The authors argue that RL system design [must] jointly account for many tightly coupled factors, yet the functional interdependencies between algorithmic components remain underexplored. Through a systematic investigation on continuous-control tasks, they find that "the efficacy of different components exhibits significant task-dependency, and naively stacking state-of-the-art techniques does not necessarily yield performance gains; instead, it often triggers emergent challenges, such as compounded non-stationarity." Based on these findings, the authors distill design principles and propose ROSER—a framework coordinating three critical dimensions: Model-based Representation (R), Optimization Stability (OS), and Experience Replay (ER). Across diverse continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves 17.60% gains over naive stack.

The paper notes that improving sample efficiency has become a central challenge in RL, with recent literature addressing it from many perspectives: representation learning, optimization stability, prioritized sampling, and advanced exploration. However, "most existing research focuses on improving sample efficiency through isolated components of the learning pipeline. It remains unclear whether different techniques for improving sample efficiency exhibit synergy, enhancing or counteract each other." The work is motivated by both research and practical considerations: a deeper understanding of interdependencies provides systematic guidance for next-generation techniques, and unified frameworks are essential for mastering complex tasks with minimal environment interaction.

The core research questions are: How do different sample-efficiency-enhancing techniques interact in reinforcement learning, and under what conditions can they be effectively combined to yield consistent and robust performance? The investigation focuses on three representative components—R, OS, and ER. The paper's contributions are summarized as:

  1. Interaction Investigation: An analysis revealing that individual benefits do not scale linearly and can even exhibit counter-productive interference in integrated systems, demonstrating that the coordination problem is real and non-trivial.

  2. Design Principle: Three design principles to harmonize algorithmic interactions, instantiated as the ROSER framework.

  3. Empirical Validation: Extensive experiments demonstrating ROSER's superior sample efficiency and cross-environment versatility.

The investigation is built on Soft Actor-Critic (SAC) as the base algorithm, evaluated on 18 tasks (9 locomotion and 9 manipulation) drawn from four benchmarks: DeepMind Control suite (DMC), HumanoidBench (HB), Myosuite (Myo), and ManiSkill2 (MS). Every experiment is run across 8 random seeds with a fixed set of hyperparameters. Performance is reported using Interquartile Mean (IQM) with 95% stratified bootstrap confidence intervals, following Agarwal et al. [1].

The authors integrate the Simba network architecture (the representative OS technique) with various modules, comparing: (i) SAC vs. SAC+OS; (ii) SAC+R vs. SAC+R+OS; and (iii) SAC+ER vs. SAC+ER+OS. Results show that "the Simba backbone provides a consistent performance boost across all evaluated configurations. In all cases, we observe a domain-agnostic synergistic amplification effect: when integrated with other sample-efficiency-enhancing techniques, Simba consistently yields a positive synergy. Notably, case (ii) reveals that introducing R in isolation yields a negative impact, particularly on locomotion tasks. However, when integrated with OS, performance improves drastically, surpassing the individual capabilities of either component by a significant margin, demonstrating super-additive, '1+1>2' synergistic gains. The takeaway: a stable optimization network backbone is essential for fostering effective synergy among components."

The authors argue that Simba's success can be partially attributed to residual connections stabilizing micro-level information flow within neural architectures. They extend this philosophy to the macro-level by introducing R⋆ (pronounced R-StaR), a Stable Model-based Representation whose core design involves a residual-style feature fusion: learned latent embeddings are concatenated with original features before entering downstream policy and value networks. This provides an information bypass ensuring the actor and critic maintain access to stable signals. Comparing SAC+OS+R against SAC+OS+R⋆, the R⋆ variant achieves a performance boost of 24.72% in locomotion tasks and 11.51% in manipulation tasks compared to the naive combination. The takeaway: the stable information flow via residual-style bypasses is crucial for effective inter-modular synergy.

The authors introduce prioritized replay, ER(P), into SAC+OS+R⋆ and find it underperforms SAC + OS + R⋆, indicating a counteractive effect between prioritized experience replay and the previously integrated components. They conjecture that during the early stage of training, when representation learning and value estimation are still evolving, priority signals may become less reliable and introduce undesirable sampling biases. To address this, they propose Uniform-to-Prioritized replay (U2P), which schedules the prioritization exponent alpha: starting at 0.0 before t start, smoothly increasing via a quadratic schedule until t end, then remaining at alpha F. This design gradually increases the influence of prioritized replay, allowing the replay distribution to adapt progressively alongside the evolving learning process, successfully mitigating the conflict. The takeaway: Naively stacking ER on top of R⋆ and OS can be counterproductive... A scheduled prioritization strategy, U2P, improves compatibility and enables more effective component integration.

The investigation summary concludes: "optimization stability is not merely beneficial in isolation, but serves as a foundational enabler that allows other components to synergize effectively. Yet micro-level network stability alone is insufficient; scaling this principle to a macro-level architecture by ensuring a reliable information flow for representation learning further catalyzes algorithmic gains. Conversely, advanced experience replay, despite its well-known efficacy in vanilla settings, presents conflicts when naively superimposed onto a complex configuration. Together, these results highlight that achieving strong sample efficiency is a systems problem: gains emerge from coherent co-design."

The paper distills three general principles for coordinating sample-efficiency-enhancing techniques:

  • Principle 1: Optimization Stability Serves as the Foundational Backbone — A stable optimization backbone establishes favorable learning dynamics that allow other components to express their potential.

  • Principle 2: Synergy Requires Stable Information Flow — explicit bypass connections can stabilize information flow and yield substantial gains in component synergy, and the authors encourage the community to further explore residual-style bypasses as a general design guideline.

  • Principle 3: Synergy Favors Robust Coordination over Aggressive Individual Performance — overly aggressive prioritized experience replay can disrupt coordination among components and degrade performance. In contrast, more conservative and progressive designs tend to preserve compatibility.

The ROSER framework is instantiated by: (1) integrating the Simba network architecture across all functional modules (encoder, actor, critic); (2) using R⋆ for macro-level residual-style information bypass; and (3) employing U2P replay to delay prioritization until learning signals are reliable. The framework is algorithm-agnostic and can be seamlessly integrated into a wide range of off-policy continuous-control algorithms, DDPG and SAC.

ROSER is compared against vanilla SAC, single-component variants (SAC+R, SAC+OS, SAC+ER), and a Naive Stack of all components. Performance profiles (Figure 6) show ROSER achieves a significant Pareto improvement over the vanilla SAC and its augmented variants. In the initial threshold range (tau < 0.2), ROSER maintains a run fraction near 1.0, whereas baseline SAC and SAC+R show immediate degradation, indicating ROSER effectively mitigates the risk of catastrophic failure during training. In manipulation tasks, "ROSER retains a higher success density at high-performance thresholds (tau > 0.8). The performance gap between ROSER and naive stack provides empirical evidence that principled coordination is crucial for unlocking the full potential of RL systems. Learning curves confirm ROSER distinguishes itself through both rapid initial convergence and a higher final performance plateau across a diverse task suite, making it environment-agnostic and robust."

To disentangle standalone benefits from integration-driven synergies, the authors evaluate R⋆ and U2P at two levels: isolated (on vanilla SAC) and synergistic (within full ROSER). Results from Table 1 show:

  • R⋆: "R⋆ degrades performance (-0.015) in the simplified setting, yet yields a clear improvement (+0.143) when integrated into the ROSER framework. This reversal indicates that R⋆ acts as a system-level stabilizer addressing joint optimization coupling, rather than a standalone optimizer."

  • U2P: while U2P degrades performance (-0.085) in the standalone SAC-ER baseline, it yields a performance boost (+0.042) within the ROSER framework, suggesting its primary value lies in buffer-level regularization, which mitigates the compound non-stationarity emergent from multi-module integration.

The experimental summary states: "the benefits of isolated modules are highly sensitive to task characteristics, suggesting that individual sample-efficiency-enhancing techniques may have inherent limitations, making it difficult for them to address tasks with specific challenges or requirements."

The paper concludes that "individually successful enhancements exhibit surprisingly divergent behaviors when jointly integrated, implying that simply stacking these modules does not guarantee additive performance gains. Rather, their interactions are complex and demand principled coordination. The authors formalize three design principles and instantiate ROSER, which not only stabilizes the learning trajectory but also achieves superior performance in both locomotion and manipulation tasks. The work highlights the importance of analyzing and improving RL sample efficiency from a synergistic, multi-module perspective."

Limitations acknowledged by the authors include: (1) a systematic taxonomy of which environment features favor specific techniques is not yet established; (2) integration strategies like R⋆ and U2P are primarily empirically-grounded rather than theoretically optimal; (3) only three components were studied, leaving out factors like exploration; and (4) findings are robust under SAC but require extension to other off-policy algorithms and real-world benchmarks for broader generalizability.

Improvements for AI systems

Improvements I can make:

  1. Replace naive module stacking with a coordinated integration policy. When combining representation learning, optimization stability, and prioritized replay, I can use progressive scheduling and compatibility checks so that components reinforce rather than undermine each other — directly addressing the paper's finding that naive stacks trigger compounded non-stationarity.

  2. Install an optimization-stable network backbone (Simba-style residual architecture) across encoder, actor, and critic as the foundational layer of any off-policy continuous-control agent, before adding auxiliary sample-efficiency modules. This converts isolated component gains into super-additive "1+1>2" system gains.

  3. Add macro-level residual-style information bypasses (R⋆): concatenate learned latent representations with raw input features before feeding downstream policy and value networks. This stabilizes cross-module information flow and prevents representation drift from corrupting the policy — shown to yield +24.72% locomotion and +11.51% manipulation gains over naive combinations.

  4. Replace fixed prioritized replay with a Uniform-to-Prioritized (U2P) schedule that ramps the prioritization exponent α quadratically from 0 to its target value only after initial training has stabilized. This avoids the counterproductive sampling bias caused by unreliable priority signals early in training.

  5. Apply the paper's three design principles as a system-level checklist: (a) stable optimization first, (b) explicit bypass connections for information flow, (c) conservative coordination over aggressive individual module tuning. This prevents the common failure mode where an individually strong module (e.g., aggressive prioritized replay) disrupts system-wide coordination.

  6. Switch from isolated-module evaluation to integration-aware testing. Since a module can degrade standalone performance yet strongly benefit the full system (as R⋆ does: −0.015 isolated, +0.143 integrated), I can evaluate every candidate component within the target system and reject modules based on system-level, not component-level, impact.

What the improved AI system can do:

  • Learn continuous-control policies with dramatically higher sample efficiency, achieving 17.60% performance gains over naive component stacking and clear Pareto improvements over vanilla SAC across 18 locomotion and manipulation tasks.

  • Train stably from scratch with near-zero catastrophic failure early in training (run fraction near 1.0 at low performance thresholds), whereas vanilla SAC and naive stacks degrade immediately.

  • Reach high success densities at demanding performance thresholds (τ > 0.8) in manipulation tasks, meaning it can execute precise, dexterous control reliably when trained with limited data.

  • Generalize across diverse benchmarks (DMC, HumanoidBench, Myosuite, ManiSkill2) with a fixed hyperparameter set — no per-task tuning required, making deployment to new continuous-control domains robust.

  • Adapt its replay strategy dynamically as learning matures: safe uniform sampling early, smooth transition to prioritized sampling later, reducing instability while retaining the speedup of prioritization.

  • Serve as an algorithm-agnostic upgrade: it can be seamlessly integrated into both SAC- and DDPG-based systems, and the design principles (stability backbone, residual bypasses, conservative coordination) can be extended to other off-policy RL agents beyond those studied in the paper.

Abstract

Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors. Despite advances in individual algorithmic components, their functional interdependencies remain underexplored: do they exhibit mutual synergy or counterproductive interference? To bridge this gap, we conduct a systematic investigation and find that the efficacy of different components exhibits significant task-dependency, and naively stacking state-of-the-art techniques does not necessarily yield performance gains; instead, it often triggers emergent challenges, such as compounded non-stationarity. Building upon these findings, we distill a suite of actionable insights into the principled coordination of these components. Guided by these insights, we propose ROSER, an RL framework that coordinates three critical dimensions: Model-based Representation, Optimization Stability, and Experience Replay. Across diverse continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves 17.60% gains over naive stack. Our findings underscore the necessity of a holistic perspective in RL system design and paves the way for developing sample-efficient agents.

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