mlsanityData to 14 Oct 2026

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following

arXiv 2510.14420 ↗First posted 16 Oct 2025cs.CL cs.AIcited by 6

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks. We propose a label-free self-supervised RL framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training. Our approach introduces constraint decomposition strategies and efficient constraint-wise binary classification to address sparse reward challenges while maintaining computational efficiency. Experiments show that our approach generalizes well, achieving strong improvements across 3 in-domain and 5 out-of-domain datasets, including challenging agentic and multi-turn instruction following. The data and code are publicly available at https://github.com/Rainier-rq/verl-if

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 25 of its 37 references that are in the map. First posted on arXiv 16 Oct 2025.

Builds on

25 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025VerIF: Verification Engineering for Reinforcement Learning in Instruction Following11 Jun

Other references · 24

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  4. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  6. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  7. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  8. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 868
  9. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
  10. 2025Generalizing Verifiable Instruction Followingcited by 194
  11. 2025MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMscited by 180
  12. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
12 more
  1. 2025Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learningcited by 161
  2. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 152
  3. 2025WritingBench: A Comprehensive Benchmark for Generative Writingcited by 106
  4. 2023COLLIE: Systematic Construction of Constrained Text Generation Taskscited by 70
  5. 2024CFBench: A Comprehensive Constraints-Following Benchmark for LLMscited by 67
  6. 2025When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMscited by 53
  7. 2025Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Modelscited by 50
  8. 2025AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarioscited by 49
  9. 2025RewardAnything: Generalizable Principle-Following Reward Modelscited by 28
  10. 2025Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Modelscited by 12
  11. 2025MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Trainingcited by 12
  12. 2025From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Precision over Diversity: High-Precision Reward Generalizes to Robust Instruction Followingcited by 1
  2. 2026SEIF: Self-Evolving Reinforcement Learning for Instruction Followingcited by 1
  3. 2026LsrIF: Enhancing Logic-Structured Instruction Following of Large Language Modelscited by 0
  4. 2026From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Taskscited by 0
  5. 2026Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimizationcited by 0
  6. 2026TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensemblescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
  2. 2025Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following
  3. 2025RLSR: Reinforcement Learning with Supervised Reward Outperforms SFT in Instruction Following
  4. 2026Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
  5. 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followers
  6. 2026Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy
  7. 2025Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models
  8. 2025IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards