e1: Learning Adaptive Control of Reasoning Effort
cs.AI, cs.LG
Submitted: 2025-10-30
Updated: 2026-10-10
Terminology
Sources
- AI Agents as Universal Task Solvers
- gpt-oss-120b & gpt-oss-20b Model Card
- L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
- Training Language Models to Reason Efficiently
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large Language Models
- Measuring Massive Multitask Language Understanding
- Measuring Mathematical Problem Solving With the MATH Dataset
- Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
- s1: Simple test-time scaling
- Qwen2.5 Technical Report
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- HybridFlow: A Flexible and Efficient RLHF Framework
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Kimi k1.5: Scaling Reinforcement Learning with LLMs
- LATTS: Locally Adaptive Test-Time Scaling
- Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
- Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning
- GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models
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