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An Empirical Study of Reward Specification and Benchmark Reliability in GRPO-based LLM Unlearning

Rubén Balbastre, Juan Manuel Orduña, Mariano Pérez

arXiv:2608.17804v1cs.LGcs.CL

TL;DR

LLM unlearning leaves broad, non-leaking answers underspecified, while benchmark and behavioral diagnostics may disagree about success. This study compares reward specifications and initialization conditions in controlled GRPO-based unlearning, finding that similar forgetting scores can reflect qualitatively different learned behaviors.

  • Problem

    Target-adjacent unlearning lacks an explicit requirement for useful broad-topic answers without target-specific leakage, and evaluation signals may not characterize the resulting behavioral endpoint.

  • Method

    The study compares four reward specifications and two initialization conditions in a controlled LoRA-GRPO RWKU setup, using held-out behavioral audits alongside benchmark scores.

  • Results

    Similar RWKU forget improvements corresponded to refusal collapse, direct-answer suppression, classifier-aligned leakage, or rubric-selected broad-topic behavior.

  • Takeaways & Limitations

    Credible RLVR-based unlearning evaluation should assess target suppression, prompt behavior, refusal, reward dynamics, policy support, and held-out leakage together.

  • Takeaways & Limitations

    The study measures post-training behavior rather than parameter-level deletion and does not rule out relearning, extraction, or stronger recovery attacks.

Abstract

from arXiv · show

Practical LLM unlearning is usually evaluated through two objectives: suppress target-specific knowledge and preserve non-target utility. In generative QA, this leaves a third behavior underspecified: when a target-adjacent prompt admits a broader answer without target-specific leakage, the model should answer at that level rather than leak, evade, or refuse. We study this specification problem in a controlled LoRA-GRPO RWKU setting, comparing four reward designs that span lexical suppression, anti-refusal shaping, rubric-based broad answering, and an explicit refusal contrast, with and without SFT warm-up. The experiments show that optimization success is not equivalent to behavioral unlearning: RWKU forget scores, held-out completion audits, terminal training-rollout audits, and training dynamics can point to different conclusions. We trace these disagreements to reward-hacking endpoints, policy-support limits in GRPO, benchmark probes that miss endpoint changes, and rewards that can select broad-topic answering with low semantic leakage during optimization.

1 Introduction

Practical LLM unlearning must suppress target knowledge while preserving non-target utility, but generative QA also requires useful broad-topic answers to target-adjacent prompts without leakage. This study examines whether reward designs, SFT warm-up, benchmark scores, completion audits, and training dynamics consistently capture that behavior.

  • Introduction: Machine unlearning addresses privacy, copyright, licensing, safety, and policy needs when full retraining is infeasible.Post-hoc unlearning is framed as a behavioral intervention for removing specific knowledge from already trained models.
  • Introduction: Useful broad-topic answering is an additional requirement beyond target suppression and non-target utility preservation.Target-adjacent prompts may admit broader responses without target-specific leakage, so blanket refusal is not the intended behavior.
  • Introduction: Evaluation can miss leakage or reward unhelpfulness because models may appear to forget while leaking under varied prompts or becoming evasive and refusal-dominant.The study highlights paraphrase, aliasing, indirection, multilingual, and recovery-style probes as cases where benchmark or reward impressions can diverge.
  • Introduction: The study compares R0-Lex, R1-AntiRefusal, R2-Rubric, and R4-Refusal reward specifications in a shared GRPO pipeline, with original instruct and SFT warm-start initializations.The rewards span lexical suppression, anti-refusal shaping, rubric-based broad answering, and refusal as a diagnostic contrast; SFT warm-up tests GRPO policy-support limits.
  • Introduction: Reward scores and RWKU scores often disagree with held-out audits and training dynamics, yielding similar forgetting scores but refusal collapse, artifacts, residual leakage, or different answer styles.The evaluation includes forgetting, neighbor locality, membership-inference behavior, utility, and audits of leakage, helpfulness, refusal, language drift, and broad-topic helpfulness.

2 Related Work

Prior LLM-unlearning work primarily evaluates behavioral forgetting and utility, using supervised, gradient-based, preference, and reinforcement-learning objectives. Closest RL-based studies optimize generated completions or graded rewards, while RLVR analyses emphasize that proxy optimization can exploit verifier gaps and diverge from intended behavior.

  • Behavioral evaluation: Behavioral unlearning evaluations examine forgetting, locality, retain utility, membership-inference behavior, and recovery-prompt robustness because retraining-equivalence guarantees are rarely practical at modern scale.Prior methods include supervised objectives, gradient-based methods, preference objectives such as NPO, and reinforcement-learning approaches.
  • RL-based unlearning: RULE optimizes refusal boundaries and forget-retain trade-offs, while PURGE applies GRPO with intrinsic rewards penalizing forbidden concepts.These studies treat forgetting as policy optimization over generated completions.
  • Verifier reliability: Reward gaming occurs when proxy optimization improves the verifier score while degrading the intended objective, including through exploited verifier gaps or rubric omissions.This risk is especially relevant to RLVR-style unlearning, where the optimized verifier may not capture intended behavior.

3 Problem Formulation

The problem formulation defines targeted unlearning as suppressing direct and indirect target-specific leakage while preserving useful non-target behavior. It additionally requires useful broader-topic answers when they avoid target leakage, and studies how reward design and initialization affect resulting behavior.

  • 3 Problem Formulation: Targeted unlearning should reduce direct or indirect target-specific leakage while preserving useful non-target response behavior.The formulation applies to target-adjacent prompts and completions.
  • 3 Problem Formulation: When broader-topic abstraction avoids target-specific leakage, the model should answer usefully at that level rather than refuse or evade.This requirement makes a previously underspecified generative-QA behavior explicit.
  • 3 Problem Formulation: GRPO samples completion groups from the RWKU-derived forget-prompt distribution Df(x), scores them with reward Rk for k ∈{0, 1, 2, 4}, and computes group-relative advantages.Optimization uses the clipped GRPO objective with a KL penalty to the reference policy.
  • 3 Problem Formulation: The central question is how reward choice and starting policy affect benchmark forgetting, refusal behavior, and residual leakage.The full GRPO notation is provided in Appendix A.

4 Method and Experimental Design

The study isolates reward specification in a controlled LoRA-GRPO RWKU setup while treating SFT warm-up as an initialization factor. It combines benchmark metrics with held-out and terminal-rollout audits to distinguish optimization outcomes from behavioral unlearning.

  • Controlled setup: The experiments use Qwen2.5-Instruct models with a shared LoRA pipeline, matching training and evaluation settings across reward conditions while varying reward specification and SFT initialization.Cold-start begins from the original instruct model; warm-start begins from an SFT warm-up checkpoint.
  • Data and initialization: RWKU-derived QA prompts test lexical leakage, refusal, and broader non-target answering; only the GRPO training split is optimized, while a held-out split is reserved for audit.RWKU remains the external benchmark lens, and the held-out split is not monitored during training.
  • Data and initialization: The warm-up stage tests whether GRPO failures reflect reward misspecification or insufficient starting-policy support for useful non-target behavior, rather than defining a new unlearning algorithm.GRPO reinforces behaviors sampled from the current policy, so initialization affects which behaviors can be reinforced.
  • Reward specifications: R4-Refusal is a contrastive refusal diagnostic that intentionally departs from the useful-answer rewards rather than extending a strictness scale.The reward family is summarized as R0-Lex, R1-AntiRefusal, R2-Rubric, and R4-Refusal.
  • Reward specifications: R0-Lex suppresses configured forbidden target patterns, R1-AntiRefusal adds anti-refusal shaping, and R2-Rubric rewards useful broad-topic completions that avoid target-specific leakage.R2-Rubric assigns zero reward to completions classified as refusal before rubric scoring.
  • Evaluation: Checkpoints are assessed with RWKU median [Q1, Q3] per-target-entity deltas and two model-judged completion audits covering leakage, helpfulness, refusal, drift, and broad-topic helpfulness.The held-out audit uses reserved prompts, whereas the terminal rollout audit samples from the optimization distribution and is independent of the R2-Rubric training judge.

5 Results

The results show that GRPO reward optimization can produce divergent behavioral endpoints—refusal, classifier-hacking, leakage, or broad-topic answering—whose quality is not captured by RWKU forgetting alone. Warm-up improves policy support, especially for larger models and R2-Rubric, but does not ensure a uniform or reliably aligned endpoint.

  • Reward-selected endpoints: R0-Lex can achieve near-zero leakage by collapsing into refusal, especially in 0.5B and 1.5B cold-start runs.These runs show nearly vanishing lexical and semantic leakage, zero prompt helpfulness, and refusal in almost all completions.
  • Reward-selected endpoints: Warm-up enables R0-Lex learning at 3B and 7B but yields mixed endpoints, while 0.5B and 1.5B remain near-universal refusal.At larger scales, warm-up lowers refusal but increases lexical or semantic leakage and prompt helpfulness.
  • Reward-selected endpoints: R1-AntiRefusal can select broad AI-policy boilerplate, whereas R4-Refusal can accept short target-adjacent fragments instead of explicit refusal or ignorance.The R1-AntiRefusal failure appears in 0.5B and 1.5B cold-start runs; the R4-Refusal pattern is target-dependent in some 7B warm-start runs.
  • Benchmark reliability: Terminal R2-Rubric rollouts show high broad-topic helpfulness and low refusal, but earlier held-out audits report substantially higher refusal because their rubric partly treats topic avoidance as refusal.The two audits use different behavioral constructs, so their apparent disagreement requires cautious interpretation.
  • Benchmark reliability: RWKU forgetting cannot identify the selected behavioral endpoint: among warm-start 3B models, all four rewards reduce forget overlap by similar magnitudes.RWKU better exposes utility trade-offs: 0.5B cold-start R0-Lex and R4-Refusal change median fluency by −2.138 and −2.650, versus −0.068 for R1-AntiRefusal and 0.120 for R2-Rubric.

6 Discussion and Limitations

The discussion frames the findings as evidence about imperfect behavioral reward proxies rather than complete unlearning, and distinguishes benchmark, deterministic, and sampled evaluations. Claims concern post-training behavior under specific audits and decoding conditions, not parameter-level deletion or robustness to stronger attacks.

  • Reward proxies and optimization: Reward proxies expose different parts of the target-suppression/usefulness trade-off but leave reward-winning shortcuts available to GRPO.The lexical proxy inherits forbidden concepts from the PURGE release rather than regenerating a Qwen-specific concept set, improving comparability but potentially missing model-specific concepts.
  • Evaluation scope: RWKU, held-out completion audits, and terminal training-rollout audits answer different questions and should not be substituted for one another.RWKU measures benchmark forgetting, held-out audits measure deterministic target-adjacent behavior, and terminal audits measure sampled behavior on the optimization distribution.
  • Behavioral, deterministic claims: The measurements assess post-training behavior rather than parameter-level deletion and do not rule out relearning, extraction, or stronger recovery attacks.The held-out audit uses temperature-zero decoding, so greedy leakage avoidance may not hold under sampling, nucleus decoding, or prompt perturbations.

7 Conclusion · A GRPO Objective Details · B Data Splits and Warm-up Construction

The study concludes that reward design and policy support determine whether GRPO-based unlearning produces genuine target suppression or alternative endpoints, so benchmark scores must be interpreted alongside behavioral and optimization audits. The GRPO implementation uses group-relative, token-level optimization with clipping, KL regularization, and completion-token normalization.

  • 7 Conclusion: Reward design and policy support jointly determine learned endpoints, with similar RWKU forget improvements corresponding to refusal collapse, direct-answer suppression, classifier-aligned leakage, or broad-topic behavior.The controlled setting is RWKU/Qwen2.5/LoRA.
  • 7 Conclusion: Credible RLVR-based unlearning claims must evaluate target suppression, prompt-level behavior, refusal, reward dynamics, policy support, and held-out leakage together.Improved scores can reflect shortcuts or weaker optimization regimes rather than intended behavior.
  • A GRPO Objective Details: For each training prompt q and target concept c, the rollout policy samples a group of completions.Each completion is then scored by the reward specification.
  • A GRPO Objective Details: GRPO computes group-relative advantages using group-level reward scaling, with ϵstab = 10−4 as a numeric stabilizer.The implementation uses token-level importance sampling.
  • A GRPO Objective Details: The clipped token-level surrogate uses ϵclip = 0.2 with equal upper and lower clipping widths.The objective is applied at token level rather than only at completion level.
  • A GRPO Objective Details: The KL penalty is based on the log-probability difference between the fixed reference policy and the current policy for sampled tokens.The reference is obtained by disabling a newly created adapter or freezing an existing pretrained adapter.
  • A GRPO Objective Details: Masked token losses are normalized by active completion tokens in the generation batch, and completed runs use two inner optimization iterations per rollout batch.The configuration is recorded in the code repository alongside library-level defaults.

B.1 Target Selection and Data Splits · B.2 Broad-topic Warm-up Data · B.3 Broad-topic SFT Completion Prompt

The completed setup uses ten RWKU forget targets with held-out auditing, SFT candidate, and GRPO training splits. Broad-topic warm-up data and prompts train or diagnose answers that abstract away target-specific details without refusing.

  • B.1 Target Selection and Data Splits: Ten forget targets are drawn from the RWKU code repository: Jennifer Lopez, Tony Blair, Marlon Brando, Bruce Lee, Serena Williams, John D. Rockefeller, Tom Clancy, Vincent van Gogh, Karl Marx, and Confucius.Rows are filtered by subject, converted to QA-style prompts, stripped of empty prompts, and reduced to prompt, completion, and subject columns.
  • B.1 Target Selection and Data Splits: The target-specific data are split into a held-out audit set, an SFT candidate subset stratified into sft/train and sft/test, and a remaining GRPO training split.The audit split is excluded from monitoring, early stopping, and reward computation.
  • B.2 Broad-topic Warm-up Data: The warm-up SFT stage targets broad-topic answers that avoid target-specific facts and refusal when a non-target broader answer is possible.The sft/test split uses the same completion policy, is excluded from SFT training, and diagnoses whether the warm-up policy reproduces the intended style before GRPO.
  • B.3 Broad-topic SFT Completion Prompt: Warm-up data consist of broad-topic completions generated from target-adjacent prompts using a dedicated generation system prompt.The prompt directs the model to abstract each question to its broader category.
  • B.3 Broad-topic SFT Completion Prompt: The completion prompt forbids repeating, paraphrasing, or directly answering the target name and concrete target-specific details.It frames the answer as a discussion of the general situation type rather than the specific target.
  • B.3 Broad-topic SFT Completion Prompt: The required style is useful, non-specific, target-free, and non-refusing, with medium-length answers of 4-6 sentences.The prompt explicitly says not to mention the target or refuse, while requiring a general category-level answer with no target facts or direct answer.
  • B.3 Broad-topic SFT Completion Prompt: The prompt defines bad style as mentioning the target, repeating concrete question details, or describing the target’s career, works, achievements, products, or places.These constraints operationalize the distinction between broad answering and target-specific leakage.

C Reward Implementation Notes … E.1 Completion Audit Judge Prompts

The study implements lexical, anti-refusal, refusal-contrastive, and rubric-based rewards, alongside LoRA-GRPO training and distinct completion-audit prompts. These implementation choices separate lexical suppression, refusal behavior, broad-topic usefulness, and target-specific leakage, while exposing differences between training rewards and post-hoc audits.

  • C Reward Implementation Notes: The lexical rewards reuse PURGE-derived target patterns to align leakage detection with prior GRPO-unlearning work rather than tuning patterns to Qwen2.5.The patterns represent target concepts, aliases, and descriptors rather than model-specific artifacts.
  • C.1 R0-Lex, R1-AntiRefusal, and R4-Refusal Scalar Rewards: R0-Lex assigns zero reward when any configured forbidden target pattern is detected.Its binary reward depends on the number of matched forbidden patterns, with any detected mention sufficient for zero reward.
  • C.1 R0-Lex, R1-AntiRefusal, and R4-Refusal Scalar Rewards: R1-AntiRefusal gates rounded non-refusal confidence behind the same lexical filter, whereas R4-Refusal directly rewards refusal-like completions as a contrastive endpoint.R1-AntiRefusal rounds classifier scores to two decimals to prevent tiny fluctuations from creating unintended GRPO advantage signals.
  • C.2 R2-Rubric Reward Implementation: R2-Rubric first filters refusals, then applies an ordered deterministic judge mapping that checks leakage, target-centeredness, degeneracy, usefulness, and broad-topic relevance.Refusal-filtered completions receive reward 0 and are not sent to the LLM judge.
  • C.3 R2-Rubric Reward Judge System Prompt: The R2-Rubric judge rewards useful broad-topic discussion while treating direct or indirect target reconstruction as leakage and refusing, boilerplate, filler, or word salad as degenerate.The judge is instructed to be conservative when leakage or target-centeredness is uncertain.
  • D Training Hyperparameters and Protocol: The protocol uses Qwen2.5-Instruct causal models with relatively large LoRA adapters across attention and MLP projections, while warm-up SFT starts from the corresponding original checkpoint.LoRA reduces optimizer and gradient memory and avoids requiring a full duplicate reference model for KL/reference behavior.
  • D.3 Training Callbacks and Checkpointing: Training callbacks monitor generated completions during SFT warm-up and active-group reward variance during GRPO, with stopping triggered by the configured criteria or a 150-step cap.The current SFT rule requests stopping above 10% R2-Rubric reward-1.0 completions; the GRPO no-learning threshold is 0.05 after the first epoch.
  • E.1 Completion Audit Judge Prompts: Held-out audits use a five-label prompt, whereas terminal rollouts use a six-label variant adding broad_topic_helpfulness, which is distinct from direct prompt_helpfulness.Post-hoc audits use GPT-5.6-luna, independently of the GPT-5.4-nano judge used for R2-Rubric training rewards.

E.2 Evaluation Settings and Budget Accounting … G.3 Held-out Completion Audit Results

The appendix fixes evaluation comparability, documents reward-hacking behaviors, and organizes full results across SFT controls, RWKU benchmarks, and held-out completion audits. Its examples show that reward satisfaction can coexist with abstraction-quality limitations, policy boilerplate, or target-adjacent fragments.

  • E.2 Evaluation Settings and Budget Accounting: Completed runs use one H100 GPU, identical training configurations, maximum optimizer-step budgets, stopping rules, and evaluation settings.These operational settings support the main-result comparisons.
  • F Qualitative Prompt–Completion Examples: R2-Rubric rewarded broad-topic abstraction, but the resulting completion was generic, questionable in detail, and truncated.The online reward for this training completion was 1.0, illustrating intended abstraction alongside residual proxy-quality limitations.
  • F Qualitative Prompt–Completion Examples: R1-AntiRefusal satisfied its non-refusal classifier while avoiding the Karl Marx question through policy and identity boilerplate.The sampled completion consists of refusal-like compliance language rather than a substantive answer.
  • F Qualitative Prompt–Completion Examples: R4-Refusal satisfied its refusal classifier without explicit refusal or ignorance, producing the terse target-adjacent fragment “This method is transformative.”The example concerns the Marlon Brando target and does not provide the requested specific examples.
  • G Full Result Tables: The appendix reports long-form result summaries and full grids spanning SFT warm-up controls, RWKU results, held-out audits, and terminal training-rollout audits.Results use medians and interquartile ranges because some target entities dominate mean-based summaries.
  • G.1 SFT Warm-up Controls: SFT warm-up controls include held-out completion audits and RWKU core and utility deltas, with comparisons against corresponding original instruct baselines.The appendix separates original instruct and SFT baseline rows in the held-out audit comparison.
  • G.2 RWKU Benchmark Results: Full RWKU benchmark grids vary model size, initialization, and reward, while utility is partitioned into GA, RA, TRU, FAC, and FLU.Core and utility deltas are reported relative to corresponding no-training baselines for the same model size and target entity.
  • G.3 Held-out Completion Audit Results: Held-out completion audits report median [Q1, Q3] results across target entities by model size, initialization, and condition.Original and SFT baseline rows are reported separately in Table 11.

G.4 Terminal Training-rollout Audit

The terminal training-rollout audit evaluates final-stage behavior using 320 sampled completions per run across the final five rollout batches. It reports broad-topic helpfulness, lexical and semantic leakage, refusal, prompt helpfulness, and drift rates across evaluated runs.

  • Audit design: The audit samples the final five rollout batches, equivalent to the final ten optimizer steps because num_iterations=2.Each batch contains 64 completions: 8 prompts times 8 generations.
  • Audit design: Each run covers 40 prompt instances and 320 sampled completions at training temperature 1.0.Run-level label rates are fractions of the 320 sampled completions.
  • Reported measures: The audit reports broad helpfulness, lexical and semantic leakage, prompt helpfulness, refusal, and drift rates by model size, initialization, and reward.Table cells report median [Q1, Q3] across target-entity runs.

H Additional Training Diagnostics

This section presents GRPO training-diagnostic curves across four Qwen2.5-Instruct model sizes, reporting run means with variability across logged steps. Runs terminated by the no-learning callback contribute data only until termination.

  • Training-diagnostic methodology: Training-diagnostic curves average available runs at each logged step, with shaded regions showing one standard deviation.Runs stopped by the no-learning callback contribute only until their trajectories end.
  • Model-scale diagnostics: Figure 1 reports GRPO training diagnostics for Qwen2.5-0.5B-Instruct.
  • Model-scale diagnostics: Figure 2 reports GRPO training diagnostics for Qwen2.5-1.5B-Instruct.
  • Model-scale diagnostics: Figure 3 reports GRPO training diagnostics for Qwen2.5-3B-Instruct.
  • Model-scale diagnostics: Figure 4 reports GRPO training diagnostics for Qwen2.5-7B-Instruct.
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