Hi-TTRL: Regulating Consensus with Hints for Test-Time Reinforcement Learning

arXiv:2608.03545 · cs.CL · Submitted 2026-08-04 · Read on arXiv

Kunbin Xu, Xingzuo Li, Xuefeng Bai, Kehai Chen

cs.CL

Submitted: 2026-08-04

Comments: 15 pages, 7 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 27× in this paper · explained in 4 episodes
• Accuracy — 23× in this paper · explained in 1 episode
• GRPO — 22× in this paper · explained in 6 episodes
• Benchmark — 17× in this paper · explained in 2 episodes
• Pseudo-labels — 16× in this paper · explained in 2 episodes
• Actor — 10× in this paper · explained in 3 episodes
• Steering — 9× in this paper · explained in 2 episodes
• RLVR — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Papers — 5× in this paper · explained in 3 episodes
• Contrastive Learning — 3× in this paper · explained in 9 episodes
• Generalization — 3× in this paper · explained in 14 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• Distribution Matching — 2× in this paper · explained in 4 episodes
• Generator — 2× in this paper · explained in 2 episodes
• Group Relative Policy Optimization — 2× in this paper
• Group Relative Policy Optimization (GRPO) — 2× in this paper · explained in 10 episodes
• Ablation Study — 1× in this paper · explained in 3 episodes
• Composition — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 3 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Power Distribution — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Reasoning Traces — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 26 episodes
• Reinforcement Learning with Verifiable Rewards (RLVR) — 1× in this paper · explained in 3 episodes
• Reward Hacking — 1× in this paper · explained in 11 episodes
• Sample Efficiency — 1× in this paper · explained in 7 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• On Designing Effective RL Reward at Training Time for LLM Reasoning
• Measuring Mathematical Problem Solving With the MATH Dataset
• R-Zero: Self-Evolving Reasoning LLM from Zero Data
• OpenAI o1 System Card
• A Survey on Human Preference Learning for Large Language Models
• Reasoning with Sampling: Your Base Model is Smarter Than You Think
• ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
• The Hidden Link Between RLHF and Contrastive Learning
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
• SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology
• RLAC: Reinforcement Learning with Adversarial Critic for Free-Form Generation Tasks
• Beyond Token-Level Policy Gradients for Complex Reasoning with Large Language Models
• Qwen3 Technical Report
• Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
• Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
• ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
• Learning to Reason without External Rewards

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