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RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability
Yichi Zhang, Zihao Zeng, Dongbai Li, Yao Huang, Zhijie Deng, Yinpeng Dong
TL;DR
Open-source DeepSeek-R1 models can comply with harmful and jailbreak queries, creating a safety gap for powerful reasoning systems. RealSafe-R1 addresses this with 15k safety-aware reasoning trajectories and supervised fine-tuning, improving refusal behavior while preserving general reasoning performance. The approach also introduces some over-refusal on safe inputs.
Problem
Open-source DeepSeek-R1 models often fail to reject harmful queries, particularly when malicious intent is concealed through jailbreak strategies.
Method
RealSafe-R1 uses 15k safety-aware reasoning trajectories generated from DeepSeek-R1 under explicit refusal instructions, then applies supervised fine-tuning.
Results
RealSafe-R1 improves safety across harmful-query and jailbreak evaluations while retaining strong reasoning performance across non-safety benchmarks.
Takeaways & Limitations
Safety alignment can strengthen refusal behavior without compromising the reported general reasoning capabilities of the R1 models.
Takeaways & Limitations
RealSafe-R1 sometimes over-refuses safe inputs, including lower full compliance on XSTest safe prompts for RS-8B than DS-8B.
Abstract
from arXiv · showhide
Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have been rapidly progressing and achieving breakthrough performance on complex reasoning tasks such as mathematics and coding. However, the open-source R1 models have raised safety concerns in wide applications, such as the tendency to comply with malicious queries, which greatly impacts the utility of these powerful models in their applications. In this paper, we introduce RealSafe-R1 as safety-aligned versions of DeepSeek-R1 distilled models. To train these models, we construct a dataset of 15k safety-aware reasoning trajectories generated by DeepSeek-R1, under explicit instructions for expected refusal behavior. Both quantitative experiments and qualitative case studies demonstrate the models' improvements, which are shown in their safety guardrails against both harmful queries and jailbreak attacks. Importantly, unlike prior safety alignment efforts that often compromise reasoning performance, our method preserves the models' reasoning capabilities by maintaining the training data within the original distribution of generation. Model weights of RealSafe-R1 are open-source at https://huggingface.co/RealSafe.
1 Introduction
RealSafe-R1 addresses safety weaknesses in open-source reasoning models by training safety-aligned DeepSeek-R1 variants. Its evaluations report stronger safety against harmful and jailbreak queries while preserving reasoning utility.
- Open-source DeepSeek-R1 models often fulfill harmful requests, especially when malicious intent is concealed by jailbreak strategies.This creates safety concerns for deployment in high-stakes domains.
- RealSafe-R1 uses 15k safety-aware reasoning trajectories generated under explicit refusal instructions to align distilled R1 models.The training data preserves structured reasoning outputs rather than adapting short-form safety answers.
- 0.27 and 0.10: the 32B model’s harmful scores under PAIR and PAP attacks decreased from 0.73 and 0.61, respectively.The reported reductions are on StrongReject and outperform the cited early SafeChain method.
- RealSafe-R1 substantially improves safety while merely affecting, and in some cases improving, reasoning and truthfulness performance.The authors present this as safety improvement without compromising utility.
2 Related Work
Prior work developed reasoning methods and safety-alignment techniques for language models, but safety alignment for long-form reasoning models remains insufficiently addressed.
- Large language models have advanced from prompting methods such as chain-of-thought and tree-of-thought toward autonomous reasoning for complex tasks.The cited applications include mathematics and code generation.
- Safety alignment commonly uses supervised fine-tuning, Direct Preference Optimization, and reinforcement learning with human feedback alongside safety datasets.These techniques were developed primarily in the broader language-model safety literature.
- Safety alignment for Large Reasoning Models remains relatively underexplored despite established language-model alignment techniques.
3 RealSafe-R1: Training Details
RealSafe-R1 training converts DeepSeek-R1’s latent safety awareness into explicit refusal behavior through distilled reasoning trajectories and supervised fine-tuning. The procedure targets harmful and jailbreak inputs while retaining the model’s reasoning format.
- Dataset Construction: Existing safety datasets are limited for LRMs because their long-form structured reasoning differs from short-form instruction-tuned outputs.The method therefore synthesizes safety data in an LRM-compatible reasoning format.
- Dataset Construction: DeepSeek-R1 may warn about malicious risks in its rationale while still complying, indicating safety awareness without specified safe behavior.This observation motivates explicit refusal instructions.
- Dataset Construction: The dataset combines direct harmful queries with jailbreak prompts drawn from filtered PKU-SafeRLHF examples and additional JailbreakV-28k samples.All selected prompts are treated as harmful and intended for refusal.
- Dataset Construction: DeepSeek-R1 is instructed to detect underlying risks during reasoning and clearly refuse queries involving apparent illegal or unethical violations.The instruction specifies refusal wording such as “sorry” or “cannot assist with.”
- Model Training: Supervised fine-tuning on the safety-aware trajectories trains the R1 series using a one-epoch optimization procedure.Training uses batch size 128, learning rate 5 × 10^-6, and warm-up ratio 0.1.
4 Experiments
RealSafe-R1 improves refusal behavior against harmful and adversarial prompts while largely preserving general reasoning performance, though it can become more conservative on safe inputs. Experiments compare model sizes, safety benchmarks, general benchmarks, and safety-aligned baselines.
- Safety results: RealSafe-R1 achieves stronger safety than DeepSeek-R1 across harmful-prompt benchmarks, including higher refusal rates and lower StrongREJECT scores.RS-32B scores 0.00 versus DS-32B’s 0.25 on unmodified prompts, while RS-32B reaches 81.0% versus 26.5% full refusal on unsafe XSTest prompts.
- General capability: RealSafe-R1 retains general reasoning performance comparable to or slightly better than DeepSeek-R1 across mathematics, coding, knowledge, and truthfulness benchmarks.RS-14B scores 71.43 versus DS-14B’s 66.67 on AIME 2024 and 66.95 versus 59.77 on TruthfulQA.
- Model-wise comparison: 87.0% versus 24.5%: RS-14B’s full refusal rate exceeds DS-14B’s on unsafe XSTest prompts, while WildChat rates are 73.2% versus 51.4%.The comparison shows higher refusal rates for RealSafe-R1 at the same model size across both evaluated unsafe-prompt settings.
- Scale-wise trend: Larger models tend to refuse less across both DeepSeek-R1 and RealSafe-R1, suggesting an inverse relationship between model size and refusal likelihood.Within DeepSeek-R1 on safe XSTest prompts, full refusal decreases from 18.0 in the 1.5B model to 4.8 in the 14B and 32B models.
- Conservativeness trade-off: RealSafe-R1 improves safety but can over-refuse safe inputs, with RS-8B full compliance at 56.8% versus DS-8B’s 90.8% on safe XSTest prompts.The authors characterize this as a more cautious but occasionally overly conservative response style.
- Baseline comparison: Compared with SafeChain-8B, RealSafe-R1-8B achieves lower harmful scores, higher refusal rates, and stronger scores on the reported MATH-500 and AIME 2024 comparisons.RealSafe-R1-8B scores 0.00 versus 0.19 on unmodified StrongREJECT prompts and 50.57 versus 40.48 on AIME 2024.
5 Conclusion & Limitations
RealSafe-R1 safety-aligns DeepSeek-R1 using 15k safety-aware reasoning trajectories while preserving the original generation distribution and reasoning capabilities. The aligned models improve safety but can occasionally over-refuse, motivating further refinement.
- RealSafe-R1 generates safety-aware reasoning trajectories that end in refusals to harmful queries, keeping training data aligned with the original model distribution.This approach uses the model’s inherent understanding of safety risks to avoid format mismatches between short safety answers and long-form reasoning.
- With only 15k demonstrations, RealSafe-R1 improves safety across the R1 series while preserving its powerful reasoning capabilities.
- RealSafe-R1 occasionally over-refuses, possibly because training lacks benign and general queries.The authors identify this issue as a target for follow-up work.