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 model)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 (20)
Paper 2 offers a rigorous negative result with mechanistic explanation, challenging the widespread assumption that GRPO reliably improves agents. Its controlled 18-run grid, double-dissociation analysis, and causal interventions provide deep methodological insight into when and why RL fails, with broad relevance to the RL-for-agents community. Paper 1 is a solid incremental efficiency method but occupies a crowded space of length/content-control techniques with narrower impact. Paper 2's counterintuitive findings and mechanistic rigor make it more likely to reshape practice and inspire follow-up work.
Paper 2 likely has higher scientific impact due to broader applicability and timeliness: improving reasoning efficiency in LLM post-training is widely relevant across domains and can be adopted by many model builders. CLORE’s content-level editing plus reference-free DPO is a clear methodological contribution with demonstrated gains on multiple benchmarks and compatibility with existing RL methods, supporting practical uptake. Paper 1 addresses an important healthcare need and offers interpretability/verification, but its impact may be narrower, harder to validate clinically, and more dependent on domain-specific integration and regulatory constraints.
PathCal introduces a training-free decoding method that distinguishes functional roles of reflection markers and intervenes only at uncertain states—a novel insight into LRM reasoning mechanics. Its training-free nature makes it more broadly applicable and easier to adopt than CLORE's training-based approach. The discovery that different reflection marker types have distinct functional roles and timing-dependent influence offers deeper mechanistic understanding of reasoning models. While CLORE provides solid engineering contributions for content-level optimization, PathCal's interpretability insights and zero-cost deployment advantage give it broader impact potential across the reasoning efficiency community.
GENSTRAT introduces a novel evaluation paradigm for LLM strategic reasoning with procedurally generated games, capability profiling, and a jaggedness metric—contributions that are methodologically innovative and broadly applicable across AI safety, economics, and multi-agent systems. Its evergreen, contamination-resistant benchmarking addresses a fundamental limitation of static benchmarks. CLORE offers a solid but more incremental contribution—optimizing reasoning trace efficiency via content-level editing within an established RL post-training pipeline. While useful, it targets a narrower problem (math reasoning efficiency) with less cross-disciplinary impact potential.
Paper 1 introduces a novel architecture (PCSP) addressing a significant gap in scalable NPC control for games, combining RL with LLM embeddings in an innovative way. It demonstrates strong results across multiple validation settings including commercial engine deployment, with clear practical applications in the gaming industry. Paper 2, while solid, is more incremental—it optimizes reasoning trace efficiency for LLMs using editing and DPO, building on well-established methods (GRPO, DPO). Paper 1 opens a new research direction (persona-conditioned shared policies) with broader cross-disciplinary impact spanning RL, NLP, and game AI.
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.