Papers with RL-trained models
CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions (2026.acl-long)
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| Challenge: | Existing benchmarks for Large Language Models often lack coverage for subtle corner cases . a substantial amount of effort has been applied to address this challenge . |
| Approach: | They propose a framework that generates adversarial test cases that expose latent vulnerabilities in code submissions. |
| Outcome: | The proposed framework improves the True Negative Rate (TNR) of existing datasets and generates superior adversarial cases on liveCodeBench. |
ERRV: Eliciting Efficient Reasoning through Reasoning Vectors for Policy Optimization in Large Language Models (2026.findings-acl)
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| Challenge: | Existing efforts to improve reasoning efficiency of large language models focus on modifying the reinforcement learning reward, such as adding length penalties. |
| Approach: | They propose a training framework that elicits efficient reasoning through reasoning vectors and a framework that allows the model to generate high-quality responses during reinforcement learning. |
| Outcome: | The proposed framework reduces reasoning length by 30% while maintaining stability, while retaining high accuracy. |
LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation (2026.acl-long)
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| Challenge: | Existing methods for hallucination detection are limited to short-form question answering tasks and do not generalize well to open-ended generation. |
| Approach: | They propose a method that trains LLMs to append a numerical confidence score to each generated statement during long-form generation. |
| Outcome: | The proposed method is 20 faster than traditional self-consistency methods while achieving better calibration. |
The Inner Monologue of Language Models: When Reasoning Traces Reveal More Than They Hide (2026.findings-acl)
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| Challenge: | Recent advances in large language models have enabled them to tackle complex tasks . a fundamental question is: are these models aware of what they "learn" and "think"? |
| Approach: | They define three core competencies: awareness of learned latent policies, generalization of these policies across domains, alignment between internal reasoning traces and final outputs. |
| Outcome: | The results show that RL-trained models exhibit stronger generalizability to novel tasks than SFT models but weak alignment between reasoning traces and final outputs. |