Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision

Chain-of-Thought reasoning consistently improves long-context LLM performance, and a process-supervised framework (LongRePS) that teaches models to generate high-quality reasoning paths yields significant gains over outcome-only supervision in long-context tasks.

Problem Statement

Long-context LLMs struggle to reason over extensive inputs to aggregate relevant information, yet most training signal comes from final answer supervision (outcome-only), which provides sparse feedback. While CoT prompting helps multi-step reasoning, its systematic benefits for long-context scenarios were underexplored, and existing process supervision methods lack quality assessment protocols tailored to long-context challenges.

Key Novelty

Systematic empirical demonstration that CoT benefits generalize across diverse long-context tasks and scale with increasing context length
LongRePS: a process-supervised training framework using self-sampling to bootstrap reasoning paths without requiring human-annotated chain-of-thought data
A novel quality assessment protocol specifically designed to evaluate and filter reasoning paths in long-context scenarios, enabling higher-quality supervision signal

Evaluation Highlights

In-domain improvements over outcome supervision baselines: +13.6 points (LLaMA) and +3.8 points (Qwen) on MuSiQue multi-hop QA benchmark
Cross-domain generalization: +9.3 points (LLaMA) and +8.1 points (Qwen) average improvement across diverse long-context QA tasks

Breakthrough Assessment

6/10 LongRePS is a solid and practical contribution that extends process supervision to long-context settings with meaningful empirical gains, but the core ideas (CoT + process supervision + self-sampling) are evolutionary rather than paradigm-shifting, building on well-established techniques applied to a specific and important problem.

Methodology

Step 1 - Empirical Analysis: Systematically evaluate CoT prompting across diverse long-context tasks and varying context lengths to establish that CoT benefits scale with context length
Step 2 - Reasoning Path Bootstrapping: Use a self-sampling mechanism to generate candidate reasoning paths from the model itself, avoiding costly human annotation of long-context CoT data
Step 3 - Process-Supervised Training: Apply the long-context-specific quality assessment protocol to filter/score bootstrapped reasoning paths, then train models with process-level supervision rather than outcome-only supervision

System Components

CoT Analysis Module

Systematic investigation framework that benchmarks CoT prompting across multiple long-context task types and context lengths to quantify when and why CoT helps

Self-Sampling Mechanism

Bootstraps reasoning paths by sampling multiple candidate CoT traces from the model itself, enabling scalable collection of training data without manual annotation

Long-Context Quality Assessment Protocol

A novel evaluation scheme tailored to long-context scenarios that scores reasoning path quality based on factors relevant to context aggregation and multi-step reasoning fidelity

LongRePS Training Framework

End-to-end process supervision framework that combines bootstrapped paths and quality scores to train LLMs with step-level reward signals for improved long-context reasoning

Results

Benchmark / Setting	Outcome Supervision Baseline	LongRePS (This Paper)	Delta
MuSiQue (LLaMA, in-domain)	Baseline	Baseline + 13.6 pts	+13.6 points
MuSiQue (Qwen, in-domain)	Baseline	Baseline + 3.8 pts	+3.8 points
Diverse QA Tasks (LLaMA, cross-domain avg)	Baseline	Baseline + 9.3 pts	+9.3 points
Diverse QA Tasks (Qwen, cross-domain avg)	Baseline	Baseline + 8.1 pts	+8.1 points

Key Takeaways

For long-context tasks, always incorporate CoT prompting at inference time — the benefits compound with context length, making it especially valuable for RAG, document QA, and multi-hop reasoning pipelines
Process supervision (rewarding intermediate reasoning steps) substantially outperforms outcome-only supervision for long-context fine-tuning; practitioners training long-context models should invest in step-level feedback mechanisms
Self-sampling can bootstrap high-quality CoT training data without human annotation, making process-supervised training accessible for teams without large annotation budgets — the key is pairing it with a domain-appropriate quality filter

Abstract

Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT's benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this critical observation, we propose LongRePS, a process-supervised framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance. Our framework incorporates a self-sampling mechanism to bootstrap reasoning paths and a novel quality assessment protocol specifically designed for long-context scenarios. Experimental results on various long-context benchmarks demonstrate the effectiveness of our approach, achieving significant improvements over outcome supervision baselines on both in-domain tasks (+13.6/+3.8 points for LLaMA/Qwen on MuSiQue) and cross-domain generalization (+9.3/+8.1 points on average across diverse QA tasks). Our code, data and trained models are made public to facilitate future research.