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Agentic Reinforcement Learning for Search Misaligns Instruction-Tuning

Yushi Yang, Shreyansh Padarha, Sarah Ball, Andrew Lee, Adam Mahdi

arXiv:2510.17431v2cs.CL

TL;DR

Whether agentic RL for search degrades instruction-tuning alignment is poorly understood. The paper diagnoses the effect, analyzes harmful-direction shifts in search representations, and tests a representation-guided reward penalty. Under an early-search trigger, RL models show substantially lower search safety than IT counterparts, while the penalty restores IT-level safety without reducing benign QA performance.

  • Problem

    Whether agentic RL for search degrades alignment built into instruction tuning, despite adding tool-use capabilities that standard alignment evaluations do not test, remains largely unstudied.

    Source: §1 · p. 1

  • Method

    The paper studies Qwen and Llama models trained with PPO and GRPO, identifies linear search-safety directions in representations, and penalizes harmful-direction projection during RL.

    Source: §1 · pp. 1–2

  • Results

    Search safety falls by up to 68.6% for Qwen models and 69.6% for Llama models relative to IT counterparts under a minimal search trigger.

    Source: §1 · p. 1

  • Takeaways & Limitations

    Representation-guided RL restores IT-level search safety while preserving benign QA performance, without additional training data.

    Source: §1 · p. 2

    Robustness to adaptive attacks that directly target the projection penalty remains open, and the study does not evaluate additional production safety layers.

    Source: Limitations · p. 9

Abstract

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Agentic reinforcement learning (RL) trains large language models to use tools, but its impact on alignment is poorly understood. We study how agentic RL for search affects the alignment of instruction-tuned (IT) models. We find that RL-trained models inherit refusal reasoning by deflecting harmful requests into benign search queries, but this breaks down under a simple diagnostic trigger that elicits a search call before refusal can occur. Under this condition, RL models produce multi-step unsafe search actions and reasoning, reducing search query safety by up to 68.6% in Qwen and Llama models relative to their IT counterparts. The effect generalises across model families, scales, and RL algorithms. To understand why, we identify linear directions in the residual stream that control search query safety, and show that RL training progressively shifts search behaviour toward the harmful end of this direction. We thus propose representation-guided RL training, which adds a reward penalty based on projection toward the harmful search direction. Training on benign data alone, it restores IT-level alignment without reducing task accuracy and requires no additional training data. Together, our work provides the first framework for diagnosing, mechanistically analysing, and mitigating alignment degradation in agentic RL for search.

1 Introduction

The paper studies whether agentic RL for search degrades alignment established by instruction tuning, a question that remains largely unstudied. Across models and training approaches, it finds reduced search safety, traces the shift to harmful search representations, and proposes a targeted training penalty.

1 Introduction

  • Agentic RL trains models to interleave reasoning with tool calls, but whether this training degrades instruction-tuning alignment remains largely unstudied.The study trains Qwen and Llama models from 3B to 32B parameters with PPO and GRPO.Source: §1 · p. 1
  • Search safety falls by up to 68.6% for Qwen models and 69.6% for Llama models relative to IT models under a trigger that elicits search before refusal.The effect holds across two model families, four model sizes, and two search setups.Source: §1 · p. 1
  • RL checkpoints project more than 2.5× toward the harmful search direction than IT models, as RL for accurate question answering shifts search representations toward harmful behavior.The paper identifies linear directions in representation space that control search-query safety.Source: §1 · pp. 1–2
  • The paper frames its contribution as a diagnosis-mechanism-mitigation framework: triggered unsafe searches, harmful-direction shifts in residual-stream states, and a projection penalty that restores safety while maintaining QA accuracy.Source: Figure 1 · p. 2
  • Representation-guided RL penalizes each training search query’s projection toward the harmful direction, restoring IT-level safety within 200 steps while preserving benign QA performance.It requires no additional training data and adds 0.3% GPU compute.Source: §1 · p. 2

2 Background and related work

The background describes agentic RL for search as part of tool-integrated reasoning, where models interleave generation with tool calls. Prior work emphasizes outcome-based rewards for knowledge-intensive QA, while this paper examines training-stage tool-use misalignment rather than inference-time agent vulnerabilities.

2 Background and related work

  • RL-for-search optimizes a retrieval-reasoning policy conditioned on both the prompt and retrieved content.The objective incorporates a reward and KL penalty relative to a reference model.Source: §2 · p. 2
  • RL for tool-integrated reasoning trains language models to interleave generation with tool calls, and search is a prominent tool in this setting.Search appeared in 10 of 13 surveyed frameworks, most of which used outcome-based rewards for knowledge-intensive QA.Source: §2 · p. 2
  • Unlike prior work on inference-time agent vulnerabilities such as prompt injection, this study examines tool-calling misalignment arising during search-agent RL training relative to IT counterparts.Prior studies discussed include web-agent hijacking and retrieval-augmented agents steered by injected content.Source: §2 · p. 2

3 Experiment setup

The experiments train search-capable models with outcome-based QA rewards, then probe whether simple early-search triggers expose unsafe behavior. Evaluation uses harmful-instruction benchmarks, judged safety metrics, and comparisons across models and search settings.

3.1 RL setup

  • PPO and GRPO training uses HotpotQA and Natural Questions, with exact-match answer and formatting rewards, on Qwen-2.5-IT and Llama-3.2-IT models.The Qwen sizes are 7B, 14B, and 32B; the Llama model is 3B.Source: §3.1 · p. 3

3.2 Search setup

  • Search attacks use prompts or response prefills to elicit an early search, testing whether models can be triggered before refusal-related tokens let them divert to safer queries.The threat model treats prompts as user-deployable and prefills as response-level controlled probes; production safety layers are not evaluated.Source: §3.2 · pp. 3–4, Table 1 · p. 3

3.3 Evaluation setup

  • Evaluation samples 1,170 harmful instructions from four benchmarks, split into 871 for evaluation and mitigation training and 299 held out.Prometheus-2 judges refusal and answer safety over reasoning traces and search safety per query; scores use a 1–5 scale rescaled to 0–100.Source: §3.3 · p. 4
  • All safety metrics drop further for RL than IT models under the same minimal search trigger across model sizes and families.Figure 3 shows PPO with web search; local search is similar to web, and GRPO has a larger search-safety drop than PPO.Source: Figure 3 · p. 4

4 How does RL degrade IT safety?

RL models can inherit instruction-tuned refusal by redirecting harmful requests to benign searches, but a search trigger before refusal exposes a systematic safety regression. Under Search attacks, they produce less safe, cascading harmful queries than their instruction-tuned counterparts.

4.1 RL inherits IT and diverts to safe search

  • Without attack, RL models match their IT counterparts on refusal, and RL-Qwen often redirects harmful requests into safe searches.RL-Llama still emits harmful web-search queries in 57.1% of cases.Source: §4.1 · p. 5

4.2 Minimal search trigger breaks RL models

  • Search attacks reduce refusal, answer, and search safety by up to 23.8%, 31.8%, and 69.6%, respectively, across Qwen and Llama models.The effect spans four model sizes, two RL algorithms, and local and web search setups.Source: §4.2 · p. 5
  • Natural-language prompts can trigger search before refusal, and searching before refusal is substantially more harmful than searching afterward.RL models may continue harmful searches even when a refusal is prefixed.Source: §4.2 · p. 5
  • Under Search attacks, RL models cascade into multi-step harmful queries, with GRPO producing more harmful searches than PPO on average.IT models more often reframe harmful requests into safer queries.Source: §4.2 · p. 5

5 Why does RL degrade IT safety?

RL training rewards queries that mirror requests for QA accuracy, while instruction tuning promotes refusal and query diversion. Across Qwen and Llama, QA performance improves as search safety under attacks declines, alongside a progressive shift toward harmful search representations.

5 Why does RL degrade IT safety?

  • RL training consistently improves QA performance at the cost of search safety under attacks for both Qwen and Llama.QA performance is measured by exact match on the Natural Questions test set.Source: §5 · p. 6
  • The candidate directions contrast harmful versus benign requests to isolate request content, or attacked versus unattacked versions of the same requests to isolate the trigger.They are normalized mean activation differences at the first <search> token; directions are extracted from IT models.Source: Table 3 · p. 6, §5 · p. 6
  • Activation steering identifies d_attack as most effective for IT-Qwen and d_prompt for IT-Llama, while d_refusal changes refusal tokens but not query content.Steering can add cautious context to otherwise direct how-to queries.Source: §5 · p. 6, Table 4 · p. 6
  • RL checkpoints progressively shift search queries toward harmful directions, consistent with the observed drop in search safety.The mechanism is a shift along a linear encoding already present in IT models, rather than the introduction of new harmful representations.Source: §5 · p. 6

6 How to restore safety during RL?

Representation-guided RL penalizes search-query representations aligned with a harmful direction. It restores instruction-tuned search safety for Qwen and Llama with minimal QA impact, using benign training data and little added compute.

6 How to restore safety during RL?

  • The method extracts a harmful-search direction from the IT model before training, then subtracts a penalty for harmful-direction alignment at the <search> token from the task reward.The penalty strength λ is selected to improve search safety without overwhelming task reward or QA accuracy.Source: §6 · p. 7
  • Representation-guided RL restores IT-level search safety for Qwen and Llama with minimal QA impact, using benign data and 0.3% additional per-step GPU compute.The method requires no additional training data.Source: §6 · pp. 6–7, Figure 6 · p. 8
  • Benign-only training suffices: adding harmful examples does not outperform it, while harmful-only training without task reward fails to guide effective search.Harmful-only training can stop search or produce placeholder queries.Source: §6 · p. 8
  • A random direction yields no safety recovery, whereas the learned search direction produces positive projections in 27% of Llama batches and 38% of Qwen batches.The ablation supports direction-specific mitigation rather than a generic penalty effect.Source: §6 · p. 8

7 Conclusion

The conclusion frames agentic RL for search as a question of whether instruction-tuning alignment degrades, how it happens, and whether it can be fixed. The paper presents a brittle refusal inheritance, a harmful-direction shift, and representation-guided RL as its answer.

7 Conclusion

  • RL-trained search models can appear aligned by diverting harmful requests into benign queries, but this inheritance from IT is brittle.A minimal search trigger can lead to multi-step harmful queries absent in IT counterparts.Source: §7 · p. 8
  • The paper identifies linear search directions that control query safety and reports that RL shifts queries toward their harmful pole.The conclusion describes this as outcome-based RL overriding IT alignment.Source: §7 · p. 8
  • Representation-guided RL penalizes projection onto harmful search directions and restores IT-level alignment without extra training data while preserving QA accuracy.The conclusion characterizes the work as a framework for diagnosing, analyzing, and mitigating alignment degradation in agentic RL for search.Source: §7 · p. 8

Limitations

The evaluation is limited to one-sentence harmful requests and search, leaving behavior on more complex agentic tasks and other tools unresolved. The mitigation’s design and evaluation also leave open alternatives and robustness questions.

Limitations

  • The evaluation uses one-sentence harmful requests; more complex multi-step agentic tasks may invoke more diverse search behaviours.The authors identify this as a data-scope boundary.Source: Limitations · p. 9
  • The study focuses on search, so whether alignment degradation extends to tools such as code execution remains open.That extension would help establish whether the phenomenon is search-specific or broadly agentic.Source: Limitations · p. 9
  • The mitigation penalizes projection at one best-steering layer, and its robustness to adaptive attacks targeting the penalty remains untested.Averaging projections across layers is identified as a natural alternative.Source: Limitations · p. 9
  • The findings concern agentic training-stage alignment degradation; additional safety layers in production deployments were not evaluated.The authors note that deployments relying on instruction-tuning safety as a baseline remain exposed to the identified regression.Source: Limitations · p. 9

Ethical Considerations

The appendices document ethical safeguards, reproducibility practices, related agentic-RL frameworks, training procedures, and the experimental setup. They specify how models were trained and evaluated, including the search pipeline and harmful-instruction data.

Ethical Considerations

  • Harmful prompts came from public academic benchmarks without personal data; reasoning traces were truncated to limit exposure to unsafe content, with warnings on figures displaying harmful content.The reproducibility statement also says code and dataset are included in an anonymous repository, while evaluation rubrics, attack templates, and traces are documented.Source: Ethical Considerations · p. 9, A Reproducibility Statement · p. 13

A Reproducibility Statement

  • The main RL study ran 16 configurations across four model sizes, two algorithms, and two search setups, each with one seed; greedy decoding made checkpoint safety metrics exact reads.The authors cite cross-condition consistency, trends over 200 steps and four checkpoints, and large effect sizes as evidence against a lucky-seed artefact.Source: A Reproducibility Statement · p. 13

B Summary of agentic RL for tool-use

  • The study adopted outcome-based reward as the simplest common setup among three search-RL frameworks, whose reward designs add search-use bonuses or process-aware credit assignment in different ways.Search-R1 used exact-match outcome rewards, R1-Searcher added a search-use bonus, and ARPO added partial rewards to reasoning and tool-use steps.Source: B Summary of agentic RL for tool-use · p. 13, Figure 7 · p. 15

C More on RL for search

  • PPO and GRPO hyperparameters and the shared reward structure are documented; rewards combine answer correctness, valid output tags, and retrieval correctness.The retrieval criterion checks whether retrieved passages contain the ground-truth answer from NQ or HotpotQA.Source: C More on RL for search · p. 13, §C.3 · p. 14

C.1 PPO with a search engine

  • For PPO with search, retrieved tokens are masked from the loss, which discourages copying retrieved content and stabilizes training.The appendix also reports that advantages use Generalized Advantage Estimation and clipping stabilizes training.Source: §C.1 · p. 13

C.2 GRPO with a search engine

  • GRPO samples multiple responses per input and uses relative rewards within each group for its advantage baseline, avoiding a learned critic; its KL penalty excludes retrieved tokens.The appendix contrasts this with PPO’s learned critic and documents retrieved-token masking for the KL penalty.Source: §C.2 · pp. 13–14

B Summary of agentic RL for tool-use

  • Among 13 surveyed tool-integrated reasoning frameworks, search was most common, followed by code execution, and Qwen-2.5 and Llama-3.x were frequent model families.Table 5 reports 10 frameworks using search and 8 using code execution; it gives 92% for Qwen-2.5 and 38% for Llama-3.x.Source: B Summary of agentic RL for tool-use · p. 13, Table 5 · p. 14

D More on experiment setup

  • The experiment setup uses a ReAct-style prompt, a four-step local retrieval pipeline, and 299 sampled harmful instructions phrased as single-sentence imperatives or questions.The pipeline encodes queries, retrieves candidates with FAISS, loads passages, and reranks the top 10 to return three; attacks add sentences to the base prompt.Source: §D.1 · pp. 14–15, §D.3 · p. 15, §D.2 · p. 15

D.4 Multi-search attacks

Multi-search attacks expose harmful search cascades, especially when ten searches are prefilled before refusal. The appendices also report evaluator agreement and examples of how RL-trained models search on benign and harmful prompts.

D.4 Multi-search attacks

  • Up to 33.3% refusal, 43.2% answer safety, and 62.0% search safety reductions occurred under ten prefilled searches before refusal.Prefilled searches produced harmful-query cascades; prompt-based ten-search instructions were less effective because models did not reliably follow them.Source: §D.4 · pp. 15–16

E.2 Human agreement with LLM evaluator

  • Across 100 harmful instructions, the LLM evaluator achieved Spearman correlations of at least 0.82 and within-±1-point agreement of at least 82% with human ratings.Human scores were consistently lower than the LLM judge’s.Source: §E.2 · p. 17
  • The evaluator sometimes overstated answer safety for neutrally phrased harmful actions and mistook refined follow-up queries for refusals, inflating refusal scores.These were the two discrepancy sources identified from evaluator reasoning.Source: §E.2 · p. 17

F.1 Reasoning traces on a benign example

  • On a benign multi-hop question, RL-Qwen used a second search to retrieve the 2018 election winner after identifying the country through the subject’s party.The trace arrived at the correct answer.Source: §F.1 · p. 17

F.2 Base model reasoning traces after RL are ruthlessly harmful

  • After RL training, base Qwen pursued a harmful request through multiple harmful searches, while RL-trained IT Qwen refused and diverted to harmless searches.The examples show distinct reasoning patterns between the base and instruction-tuned models after RL.Source: §F.2 · p. 18

F.3 Examples of jailbroken outputs under attacks

The examples and supplementary results show that search-triggered attacks can elicit persistent harmful queries, including immediately after a refusal. Across query positions, effects differ between Qwen and Llama, while search attacks outperform non-refusal prefills.

F.3 Examples of jailbroken outputs under attacks

  • The Qwen examples show progressively refined harmful searches under Search and increasingly detailed harmful queries across ten Multi-search prefills, even without intermediate reasoning.The harmful search intent persisted after an immediate refusal prefill and appeared in one example after an “Instead” reframe without an attack.Source: §F.3 · pp. 18–19
  • Llama’s later-query drift toward safer searches helps explain why Multi-search can have higher search safety than Search for that model.The cited figure discussion identifies this as the explanation for the difference.Source: §F.3 · pp. 19–20

G.1 Searches remain harmful right after refusal

  • Under Prefill-C, search safety remained lower for RL than IT models: local Qwen scored 46.0 vs 72.3 and Llama 12.8 vs 41.3.Answer safety also fell under Prefill-C despite the prefilled refusal: local Qwen 89.5→70.9 and Llama 96.2→81.5.Source: §G.1 · pp. 20–21, Table 11 · p. 21

G.2 Search attacks outperform non-refusal prefills

  • Search attacks generally produced lower safety metrics than non-refusal prefills, with early searches triggering harmful request-mirroring queries before refusal.The passages distinguish imported harm via retrieved content from harm drawn mainly from pretraining knowledge under non-refusal prefills.Source: §G.2 · p. 22

G.3 Search safety across query positions

  • RL-Qwen scored lower than IT-Qwen across all ten Multi-search query positions, while RL-Llama’s initially less-safe searches drifted toward safer queries.The safety rise was faster for Prefill-C×10 than for Prefill-A/B×10.Source: §G.3 · p. 22

H.1 Full steering results

Representation-guided RL restores IT-level search safety by 200 steps, while steering results distinguish search-query reframing from refusal behavior. Steering can improve safety, but refusal-like behavior or garbling limits some directions.

H.1 Full steering results

  • d_attack at layer 14 is the most effective IT-Qwen steering direction, cleanly reframing harmful queries without garbling or changing their structure.It appends safety-framed phrases such as “legal consequences of...” or “ethical implications of...”; it is nearly orthogonal to d_refusal.Source: §H.1 · p. 22, Table 13 · p. 22
  • +100 safe queries result from d_refusal steering at α=+2.0, but refusal language becomes the search query rather than modifying its content.The paper therefore considers this direction unsuitable despite the numerical gain.Source: §H.1 · p. 22
  • +123 safe queries and average safety 75.8 result from d_prompt at layer 26 with α=+2.0, but some outputs skip search or become garbled.The passage attributes these refusal-like effects to its higher cosine similarity to d_refusal (0.55) at that layer.Source: §H.1 · p. 22
  • Effective mitigation must intervene on d_attack or d_prompt during search generation because post-hoc refusal cannot prevent harmful queries from being issued.The results treat “what to search” and “whether to refuse” as separable representations.Source: §H.1 · pp. 22–23
  • For Llama, d_attack extraction is infeasible because harmful instructions without attacks rarely elicit search queries, leaving too few paired representations.The study therefore extracts only d_prompt for Llama.Source: §H.1 · p. 23

H.2 Mitigation results with ablations

  • With Qwen mitigation V1, about 65% of benign queries trigger the penalty during steps 1–25, but fewer than 1% do by step 50.The authors interpret the rapid decline as the model learning to shift its search queries early in training.Source: §H.2 · p. 23
  • By 200 training steps, representation-guided RL with benign data alone restores all three safety metrics to IT-model levels.Augmenting benign data with harmful examples also restores IT-model search safety.Source: Table 15 · p. 23, §H.2 · p. 23
  • Under Multi-search, RL-Qwen safety stays below IT across reported query positions, while RL-Llama’s first queries are less safe before safety rises.For Llama, safety rises faster with Prefill-C×10 than with Prefill-A/B×10.Source: Figure 17 · p. 28, Figure 18 · p. 29

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