is replay buffer approach for mitigating catastrophic forgetting in finetuning for LLM also somehow applicable to RL training for LLMS? if yes, give me paper references

Grok 4.3

Answer: Yes, replay buffers are applicable to RL training for LLMs to mitigate catastrophic forgetting, as all reviewed models confirm with overlapping references such as Ouyang et al. (2022).

All other models (Gemini 3.5 Flash, Claude Opus 4.7, and GPT-5.5) agree that replay mechanisms transfer effectively, whether through pretraining data mixing in PPO-ptx, experience replay of trajectories, or KL regularization to a reference policy. Their citations reinforce my prior references to InstructGPT (Ouyang et al., 2022) and add complementary works like Rolnick et al. (2019) on continual learning replay and Lin et al. (2023) on alignment tax mitigation, without introducing contradictions. No response provided evidence that replay buffers fail or require fundamental changes in the RL-for-LLMs setting, so my position is unchanged.

Gemini 3.5 Flash

Answer: Yes, replay buffer approaches are highly applicable to RL training for LLMs, serving both as data rehearsal to prevent general capability forgetting and as experience replay to stabilize policy optimization, provided off-policy challenges are algorithmically managed.

I maintain my position that replay buffer mechanisms are highly applicable and critical in LLM RL training. However, the insights from the other models—particularly the distinctions raised by GPT-5.5 and Claude Opus 4.7—highlight the need to separate this into two distinct paradigms: data rehearsal and experience replay. First, "data rehearsal" (replaying SFT, safety, or pre-training datasets) is highly effective for mitigating the "alignment tax" and preventing catastrophic forgetting of foundational capabilities during RL. This is exemplified by the PPO-ptx objective in InstructGPT (Ouyang et al., 2022) and sequential preference learning (e.g., COPR). Here, the replay buffer acts as a stabilizing anchor to the base distribution. Second, "experience replay" (reusing past RL trajectories generated by earlier iterations of the LLM policy) is valuable for sample efficiency but introduces severe off-policy challenges in on-policy algorithms like PPO. As GPT-5.5 correctly points out, replaying old rollouts can destabilize training unless corrected for policy mismatch or restricted to off-policy frameworks. In modern iterative alignment methods (like iterative DPO or Reinforced Self-Training/ReST), maintaining a buffer of high-quality historical generations and sampling from them functions exactly as a structured experience replay to systematically guide policy improvement without collapse.

Claude Opus 4.7 (changed mind)

Answer: Yes, replay-buffer approaches transfer to RL training of LLMs and are used in practice, but with an important distinction: 'replay' splits into (a) experience replay of RL rollouts (constrained by on/off-policy concerns) and (b) rehearsal of SFT/pretraining/preference data to prevent capability forgetting during reward optimization.

I'm largely maintaining my position, but GPT-5.5's answer convinced me to sharpen one point I had glossed over: the on-policy/off-policy caveat for PPO-style RLHF. My original answer treated 'experience replay of rollouts' and 'data replay of SFT/pretraining' as parallel mechanisms, but GPT-5.5 correctly emphasized that naively replaying stale PPO trajectories is algorithmically problematic without importance correction or staleness limits — they're safer as auxiliary supervised/KL/distillation signals than as fresh on-policy samples. That's a meaningful refinement worth flagging to the asker. On references, the consensus across all four answers converges on a core set I'd now recommend with more confidence: - Ouyang et al. 2022 (InstructGPT) — PPO-ptx mixes pretraining gradients into PPO updates; the canonical example of rehearsal-style replay in RLHF to combat the alignment tax. - Ziegler et al. 2019 and Stiennon et al. 2020 — early RLHF with KL-to-reference regularization (related to, but not identical to, replay). - Isele & Cosgun 2018 (Selective Experience Replay for Lifelong Learning) and Rolnick et al. 2019 (Experience Replay for Continual Learning) — directly connect replay buffers to mitigating catastrophic forgetting in RL, which is the bridge the asker is looking for. - Gulcehre et al. 2023 (ReST) and iterative DPO / self-training pipelines — use buffers of past generations across iterations. - Korbak et al. 2023 (Pretraining with Human Preferences) — distributional anchoring perspective. I'd add two cautions the other answers underweight: (1) DPO's KL term is regularization, not replay — Gemini's framing slightly conflates them; (2) the 'Continual RLHF (Roller et al., 2024)' reference Gemini cites I cannot confirm exists, so I'd advise the asker to verify it rather than cite it blindly. The same caveat applies to any title I listed from memory. So: position unchanged, but credit to GPT-5.5 for the on-policy/off-policy nuance that makes the answer more technically accurate.

GPT-5.5

Answer: Yes: replay-buffer/rehearsal ideas are applicable to RL training for LLMs, especially as pretraining/SFT-data replay or offline-RL replay, but stale PPO rollouts must be handled carefully because they become off-policy.

I maintain my previous position; the other answers reinforced it rather than changing it. The most useful clarification from them is the InstructGPT example: Ouyang et al. explicitly used the PPO-ptx objective, mixing pretraining data into PPO-style RLHF updates to reduce the “alignment tax.” That is very close to rehearsal/replay for catastrophic-forgetting mitigation. The key distinction is that there are two different things people may call “replay” in RL for LLMs. First, there is trajectory or experience replay: storing prompts, generations, rewards, logprobs, values, etc., and reusing them for RL updates. This is natural in off-policy RL, but risky in PPO-style RLHF because old samples were generated by an older policy. If reused naively, they can bias the update unless you use importance correction, limit staleness, or use an algorithm designed for off-policy/offline data. Second, there is rehearsal/data replay: mixing old SFT, pretraining, safety, preference, or earlier-task examples into the RL training loop as auxiliary losses. This is often the more practical anti-forgetting approach for LLM RL. It preserves general language ability, instruction following, safety behavior, and domain capabilities while the RL objective pushes the model toward a narrower reward model. Good references to start with: 1. Ouyang et al., 2022, “Training language models to follow instructions with human feedback.” This is the key RLHF/InstructGPT paper. Look specifically at PPO-ptx, where pretraining data is mixed into RLHF updates to reduce capability loss. 2. Stiennon et al., 2020, “Learning to summarize with human feedback.” Early RLHF with PPO for language models; important for understanding KL-to-reference-policy stabilization, which is not replay but serves a similar anti-drift role. 3. Bai et al., 2022, “Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.” Relevant for RLHF on assistants and the practical need to prevent drift away from helpful/harmless behavior. 4. Snell et al., 2022, “Offline RL for Natural Language Generation with Implicit Language Q-Learning.” Directly relevant if you mean replay/offline buffers of logged language-model generations rather than purely on-policy PPO. 5. Gulcehre et al., 2023, “Reinforced Self-Training (ReST) for Language Modeling.” Uses iterative generate-filter-train cycles and reuses model-generated data according to reward, which is replay-like in the broader RL-for-LLMs setting. 6. Ramamurthy et al., 2022, “RL4LMs: A Library for Reinforcement Learning Fine-Tuning of Language Models.” Useful practical reference for RL fine-tuning of language models and the mechanics of rollouts, buffers, and policy optimization. 7. Rolnick et al., 2019, “Experience Replay for Continual Learning.” Not LLM-specific, but a foundational continual-learning replay paper that explains why replay mitigates catastrophic forgetting. 8. Lin, 1992, “Self-improving reactive agents based on reinforcement learning, planning and teaching,” and Mnih et al., 2015, “Human-level control through deep reinforcement learning.” These are classic experience-replay references in RL generally. So the short practical answer is: yes, use replay in LLM RL, but distinguish long-term rehearsal buffers from PPO rollout buffers. For PPO/RLHF, replay old data mostly through auxiliary SFT/pretraining/preference losses or KL anchoring; for offline/off-policy RL methods, replaying stored trajectories is much more direct.

Kimi K2.6

Answer: Yes, replay-buffer approaches are applicable to LLM RL training, but their safe use depends on whether the algorithm is off-policy—where past trajectories or preferences can be replayed directly—or on-policy, where they must be adapted into data rehearsal or KL-regularized anchoring to avoid bias.

The core mechanism of replay buffers, storing past examples to interleave with new updates and prevent catastrophic forgetting, does transfer from supervised LLM fine-tuning to RL settings. In off-policy or iterative RL methods applied to language models—such as DPO (Rafailov et al., 2023), offline RLHF, or ReST (Gulcehre et al., 2023)—a buffer of past high-reward generations, preferences, or trajectories can be sampled directly to stabilize gradients and improve sample efficiency, much like DQN's experience replay (Mnih et al., 2015). However, standard on-policy RLHF (e.g., PPO as used in InstructGPT) cannot naively replay old rollouts because the policy distribution has shifted, making stale trajectories off-policy and potentially destabilizing importance-weighted updates. Instead, practitioners employ two closely related adaptations. The first is data rehearsal: mixing in a fixed buffer of pretraining or supervised-finetuning data during RL updates. Ouyang et al. (2022) explicitly used this in their PPO-ptx variant to mitigate the alignment tax. The second is functional replay via a KL-divergence penalty against a frozen reference model, which preserves prior behavior without storing samples (Ziegler et al., 2019; Stiennon et al., 2020). Recent literature also explicitly connects continual-learning replay to sequential alignment. Foundational works like Rolnick et al. (2019) and Isele & Cosgun (2018) established experience replay for continual RL, and these ideas are now being adapted to LLM scenarios where historical prompt-response pairs are buffered across RLHF stages (e.g., in asynchronous or continual RLHF pipelines). Therefore, the answer is yes, but one must distinguish trajectory replay (suitable for off-policy RL) from data rehearsal and reference anchoring (required for stable on-policy RLHF).

Final Summary

The AI Roundtable unanimously confirmed that replay mechanisms are vital for RL training to counter the 'alignment tax,' where models lose foundational capabilities during reward optimization. GPT-5.5 and Claude Opus 4.7 led the group in distinguishing between 'data rehearsal' (mixing in pre-training samples) and 'experience replay' (reusing past rollouts), noting that the latter requires algorithmic care in on-policy settings like PPO. The group frequently cited Ouyang et al. (2022) as the canonical example of using rehearsal to maintain model stability.

All 5 models agreed