CLORE: Content-Level Optimization for Reasoning Efficiency
Yuyang Wu, Qiyao Xue, Guanxing Lu, Weichen Liu, Zihan Wang, Manling Li, Olexandr Isayev
Abstract
Reinforcement learning post-training has improved the reasoning ability of large language models, but often produces unnecessarily long, repetitive, or semantically opaque reasoning traces. Existing efficient reasoning methods mainly regulate response length through explicit budgets or length-aware rewards, leaving intermediate reasoning content weakly supervised. We propose CLORE, a content-level optimization framework that improves reasoning efficiency by editing correct on-policy rollouts. CLORE uses an external augmentation model to delete repetitive segments, illegible or task-irrelevant content, and superfluous reasoning after the solution is established, while preserving the final answer. The resulting augmented--original pairs are optimized with an auxiliary reference-free DPO objective alongside standard policy-gradient training. By restricting augmentation to correct trajectories and performing local deletion, CLORE keeps edited rollouts close to the policy distribution and mitigates off-policy mismatch. Experiments on DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B across five mathematical reasoning benchmarks show that CLORE improves the accuracy--efficiency trade-off and remains compatible with GRPO, DAPO, Training Efficient, and ThinkPrune. Content-level analyses further show that CLORE reduces repetitive reasoning, illegible content, and post-answer exploration, supporting content-level supervision as a complementary direction to length-level control.
AI Impact Assessments
(1 models)Scientific Impact Assessment: CLORE: Content-Level Optimization for Reasoning Efficiency
1. Core Contribution
CLORE introduces a content-level optimization framework for improving reasoning efficiency in RL-trained LLMs. The key insight is that existing efficient reasoning methods focus almost exclusively on length-level control (budgets, length rewards), while ignoring the *quality* of intermediate reasoning content. CLORE addresses three specific pathologies: (1) repetitive reasoning segments, (2) illegible or task-irrelevant content (including garbled multilingual text, broken code), and (3) superfluous post-answer exploration.
The method works by: (a) sampling on-policy rollouts, (b) filtering for correct trajectories, (c) using an external augmentation model to perform deletion-only editing of low-quality content, and (d) training with augmented–original preference pairs via a reference-free DPO objective alongside standard policy-gradient loss. The restriction to deletion-only edits on correct trajectories is a clever design choice that keeps augmented samples close to the policy distribution, mitigating off-policy issues without requiring an explicit reference model.
2. Methodological Rigor
The experimental design is reasonably thorough. CLORE is evaluated on two base models (DeepSeek-R1-Distill-Qwen-7B and Qwen2.5-Math-7B) across five mathematical reasoning benchmarks spanning different difficulty levels. The paper demonstrates compatibility with four different RL training methods (GRPO, DAPO, Training Efficient, ThinkPrune), which strengthens the generality claim.
However, several concerns arise:
3. Potential Impact
The practical impact is moderate to significant. The framework addresses a real and growing problem—reasoning models like DeepSeek-R1 and o1 produce verbose, often illegible reasoning traces that waste compute at inference. Key impact vectors include:
4. Timeliness & Relevance
This paper is highly timely. The proliferation of reasoning-focused LLMs (o1, R1, QwQ) has created an acute need for efficient reasoning methods. The "overthinking" problem is well-documented but underaddressed at the content level. Most concurrent work focuses on length control, making CLORE's content-level perspective a welcome complement. The paper correctly positions itself as orthogonal to rather than competing with length-based methods.
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment
CLORE makes a solid and timely contribution by shifting the efficient reasoning conversation from length control to content quality. The framework is well-designed, with the deletion-only constraint and reference-free DPO providing principled solutions to the off-policy challenge. The experimental evaluation is comprehensive in terms of method compatibility but narrow in domain scope. The content-level analyses (repetition, illegibility, post-answer exploration) are the paper's strongest empirical contribution, providing quantitative evidence for qualitative improvements that length metrics cannot capture. The work opens a promising research direction but leaves significant room for extension to broader domains, larger models, and more sophisticated content quality criteria.
Generated May 22, 2026
Comparison History (15)
ExComm addresses a fundamental problem in agentic test-time scaling—error propagation in long-horizon reasoning—with a novel multi-agent communication protocol that detects cross-agent factual conflicts and resolves them through tool-based verification. This has broader impact across agentic AI systems beyond math reasoning. While CLORE offers a solid contribution to reasoning efficiency via content-level editing, it represents a more incremental improvement within the established efficient reasoning paradigm. ExComm's approach to inter-agent communication, belief updates, and trajectory diversification introduces more novel architectural concepts with wider applicability to emerging agentic AI workflows.
Paper 2 (CLORE) addresses a well-defined, timely problem in LLM reasoning efficiency with a clear methodological contribution (content-level optimization via edited rollouts + DPO). It demonstrates compatibility with multiple existing training frameworks and shows consistent improvements across multiple benchmarks, making it broadly applicable to the rapidly growing LLM reasoning community. Paper 1 presents an interesting cross-domain benchmark for coordinated AI agents, but its scope is narrower, the tasks feel disparate, and the conclusions are somewhat incremental (coordination helps sometimes but not always). Paper 2 has stronger methodological novelty and broader near-term adoption potential.
Paper 1 introduces a foundational cognitive architecture (System I, II, and III) that fundamentally improves how agents self-regulate and plan, yielding massive token efficiency and performance competitive with models 10x-30x larger across diverse domains (math, science, web). Paper 2 offers a valuable but narrower post-training data-editing technique specifically for pruning reasoning traces, which has lower theoretical novelty and broader impact compared to Paper 1's generalizable agentic reasoning framework.
Paper 1 addresses a critical and highly timely challenge in AI: improving the reasoning efficiency of LLMs. By optimizing reasoning traces to remove repetitive and irrelevant content without sacrificing accuracy, CLORE offers broad applicability across all domains utilizing reasoning models. While Paper 2 presents a strong domain-specific contribution to computational chemistry, Paper 1's methodology fundamentally enhances core LLM capabilities, promising a wider and more immediate scientific impact across the broader artificial intelligence community.
Paper 1 has higher potential impact: it demonstrates AI-driven formal proof search solving genuine open problems (Erdős, OEIS) with verified correctness via Lean, a strong rigor signal and a notable novelty milestone (large-scale open-problem evaluation). Its real-world applicability to active mathematical research across multiple domains suggests broad cross-field influence and high timeliness as formal methods + LLMs converge. Paper 2 is a solid, timely efficiency method for RL post-training with useful applications, but its contributions are more incremental within model training and likely narrower in downstream scientific disruption than verified progress on open mathematics.
Paper 2 introduces a fundamentally new evaluation paradigm for MLLMs in social cognition, exposing a critical 'Prejudice Gap' where models appear to succeed without genuine understanding. It contributes a novel task formalization (GPR), a large-scale dataset (MM-OCEAN), and benchmarks 27 models with new failure-mode metrics. This has broader impact across AI safety, fairness, and human-AI interaction. Paper 1, while solid engineering work on reasoning efficiency, is more incremental—combining known techniques (DPO, content editing) for a well-studied problem of verbose reasoning in LLMs.
Paper 2 introduces a highly novel paradigm of using latent 'think tokens' to replace explicit Chain-of-Thought generation in multimodal models. By treating reasoning as latent variables, it fundamentally addresses the computational bottleneck of CoT, offering constant inference costs while maintaining reasoning benefits. This conceptual leap offers broader architectural implications and scalability compared to Paper 1, which primarily focuses on refining and optimizing existing explicit reasoning traces via post-training.
Paper 2 likely has higher impact due to timeliness and broad applicability: improving LLM reasoning efficiency is a central current bottleneck for deployment, affecting cost, latency, and reliability across many domains. CLORE introduces a concrete, scalable training framework (content-level rollout editing + auxiliary reference-free DPO) with demonstrated gains on multiple benchmarks and compatibility with several RL post-training methods, suggesting strong methodological relevance and adoption potential. Paper 1 is novel in integrating prospect theory into strategic classification, but the application scope is narrower and likely impacts fewer ML subfields immediately.
Paper 1 targets a critical and highly timely bottleneck in modern AI: the inference efficiency and reasoning quality of Large Language Models. By introducing a content-level optimization framework to refine RL post-training for models like DeepSeek-R1, it offers immediate, high-impact applications for deploying efficient LLMs. While Paper 2 presents an elegant interdisciplinary approach to strategic classification, Paper 1's alignment with the current massive shift toward reasoning-focused LLMs ensures broader adoption, immediate real-world utility, and significantly higher visibility within the mainstream AI research community.
Paper 1 bridges mechanistic interpretability and adversarial robustness by theoretically reframing refusal ablation as a latent-space evasion attack. This principled understanding explains existing empirical successes and generates a more powerful attack mechanism with broad implications for AI safety. While Paper 2 addresses a timely issue in reasoning efficiency, Paper 1 offers deeper theoretical insights into model internals and vulnerabilities, likely driving broader foundational impact across the alignment and security communities.
CLORE addresses a timely and broadly relevant problem in LLM reasoning efficiency, proposing a novel content-level optimization framework that complements existing length-based approaches. It demonstrates compatibility with multiple training methods (GRPO, DAPO, etc.) and provides comprehensive experimental validation across multiple benchmarks and models. The work sits at the intersection of reinforcement learning and LLM post-training, areas of intense current research interest, giving it broad potential impact. Paper 2, while methodologically sound, addresses a more niche topic in safety assurance with narrower applicability and a smaller potential audience.
Paper 2 addresses a highly timely and widely applicable problem: improving the reasoning efficiency of Large Language Models. Its content-level optimization approach tackles the prevalent issue of unnecessarily long reasoning traces in RL-trained LLMs. Given the explosive growth of AI and LLMs, this work has broader cross-disciplinary impact and higher immediate relevance than the more specialized UAV sensing framework in Paper 1.
Paper 2 (CLORE) has higher potential impact: it introduces a broadly applicable training framework for improving reasoning efficiency via content-level supervision, with clear methodological contributions (edited on-policy rollouts + auxiliary reference-free DPO) and demonstrated gains across multiple math benchmarks and model families, plus compatibility with several popular RL/post-training methods. This is timely for cost/latency-critical deployment and could transfer across domains beyond math. Paper 1 is valuable as an evaluation study of live-agent behavior, but it is more task-specific (Risk, specific providers) and primarily diagnostic rather than a generalizable algorithmic advance.
Paper 1 tackles the highly relevant challenge of reasoning efficiency in large language models, a rapidly growing focus area following the advent of reasoning models like DeepSeek-R1. By introducing a novel framework to directly optimize intermediate reasoning content rather than just applying length budgets, it provides a practical method to improve the accuracy-efficiency trade-off. While Paper 2 offers interesting mechanistic insights into sycophancy mitigation via persona steering, Paper 1's methodological innovation and immediate applicability to state-of-the-art reasoning training pipelines suggest a broader and more significant impact on LLM development.
CLORE addresses a broadly impactful problem—improving reasoning efficiency in LLMs—with a novel content-level optimization framework that complements existing length-based methods. It demonstrates compatibility with multiple training algorithms (GRPO, DAPO, etc.) across multiple benchmarks, suggesting wide applicability. Paper 1, while offering interesting mechanistic insights into sycophancy via persona vectors, is narrower in scope and incremental relative to existing steering methods. The reasoning efficiency problem tackled by Paper 2 is more timely given the rapid deployment of reasoning models, and its framework is likely to see broader adoption.