The Art of Efficient Reasoning: Data, Reward, and Optimization (Feb 2026)

Описание: Title: The Art of Efficient Reasoning: Data, Reward, and Optimization (Feb 2026)
Link: http://arxiv.org/abs/2602.20945v2
Date: February 2026

Summary:
This paper systematically investigates the mechanics of efficient reasoning in Large Language Models (LLMs) using Reinforcement Learning (RL) and reward shaping. The authors reveal a two-stage training paradigm consisting of 'length adaptation' and 'reasoning refinement.' Key findings suggest that training on easier prompts provides denser reward signals to avoid length collapse, and that learned length biases can generalize across different domains. The study provides practical guidelines for optimizing data composition, rollout numbers, and reward strategies to incentivize shorter yet accurate thinking trajectories.

Key Topics:
Efficient Reasoning
Chain-of-Thought (CoT)
Reinforcement Learning (RL)
Reward Shaping
Length Adaptation
Reasoning Refinement
Prompt Difficulty
Optimization Strategies

Chapters:
00:00 - Podcast Introduction
01:40 - Solving Latency Bottlenecks
02:53 - Stage 1: Length Adaptation
04:03 - Stage 2: Reasoning Refinement
05:03 - Budget-Aware Benchmarking
06:33 - Avoiding Reasoning Collapse
07:35 - Cross-Domain Generalization
08:33 - Prioritizing Easy Data
10:21 - Scaling Rollout Counts
11:52 - Preventing Reward Hacking
13:01 - Analyzing Loophole Failures
14:13 - Optimal Sampling Strategy
15:31 - Maintaining Training Stability
17:01 - Scaling Qwen3 Results
18:48 - Evolution Of Internal Logic

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