mlsanityData to 7 Oct 2026

TACLer: Tailored Curriculum Reinforcement Learning for Efficient Reasoning

arXiv 2601.21711 ↗First posted 29 Jan 2026cs.CL cs.AIcited by 2

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Large Language Models (LLMs) have shown remarkable performance on complex reasoning tasks, especially when equipped with long chain-of-thought (CoT) reasoning. However, eliciting long CoT typically requires large-scale reinforcement learning (RL) training, while often leading to overthinking with redundant intermediate steps. To improve learning and reasoning efficiency, while preserving or even enhancing performance, we propose TACLer, a model-tailored curriculum reinforcement learning framework that gradually increases the complexity of the data based on the model's proficiency in multi-stage RL training. TACLer features two core components: (i) tailored curriculum learning that determines what knowledge the model lacks and needs to learn in progressive stages; (ii) a hybrid Thinking/NoThinking reasoning paradigm that balances accuracy and efficiency by enabling or disabling the Thinking mode. Our experiments show that TACLer yields a twofold advantage in learning and reasoning: (i) it reduces computational cost, cutting training compute by over 50% compared to long thinking models and reducing inference token usage by over 42% relative to the base model; and (ii) it improves accuracy by over 9% on the base model, consistently outperforming state-of-the-art Nothinking and Thinking baselines across four math datasets with complex problems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 20 of its 39 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

20 of 39 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning7 Jun
  2. 2025Self-Evolving Curriculum for LLM Reasoning20 May
  3. 2025How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study1 Apr
  4. 2025Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model31 Mar
  5. 2025FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models21 Mar
  6. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  7. 2025Training Language Models to Reason Efficiently6 Feb

Other references · 13

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2025s1: Simple test-time scalingcited by 1,311
  3. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Modelscited by 456
  4. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  5. 2025O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruningcited by 278
  6. 2025Reasoning Models Can Be Effective Without Thinkingcited by 182
  7. 2025DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Modelscited by 154
  8. 2025AdaptThink: Reasoning Models Can Learn When to Thinkcited by 141
  9. 2025Competitive Programming with Large Reasoning Modelscited by 115
  10. 2025An Empirical Study on Eliciting and Improving R1-like Reasoning Modelscited by 78
  11. 2025Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RLcited by 59
  12. 2025Unlocking Efficient Long-to-Short LLM Reasoning with Model Mergingcited by 58
1 more
  1. 2025Multidimensional Consistency Improves Reasoning in Language Modelscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Modelcited by 4
  2. 2026sGPO: Trading Inference FLOPs for Training Efficiency in RLVRcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2026The Art of Efficient Reasoning: Data, Reward, and Optimization
  2. 2026Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
  3. 2025Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
  4. 2025Reasoning Curriculum: Bootstrapping Broad LLM Reasoning from Math
  5. 2025AdaCuRL: Adaptive Curriculum Reinforcement Learning with Invalid Sample Mitigation and Historical Revisiting
  6. 2025Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training
  7. 2025CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs
  8. 2025Demystifying Long Chain-of-Thought Reasoning in LLMs