Honesty to Subterfuge: In-Context Reinforcement Learning Can Make Honest Models Reward Hack

## Abstract

Abstract Previous work has shown that training “helpful-only” LLMs with reinforcement learning on a curriculum of gameable environments can lead models to generalize to egregious specification gaming, such as editing their own reward function or modifying task checklists to appear more successful. We show that gpt-4o , gpt-4o-mini , o1-preview , and o1-mini — frontier models trained to be helpful, harmless, and honest — can engage in specification gaming without training on a curriculum of tasks, purely from in-context iterative reflection (which we call in-context reinforcement learning, “ICRL”). We also show that using ICRL to generate highly-rewarded outputs for expert iteration (compared to the standard expert iteration reinforcement learning algorithm) may increase gpt-4o-mini ’s propensity to learn specification-gaming policies, generalizing (in very rare cases) to the most egregious strategy where gpt-4o-mini edits its own reward function. Our results point toward the strong ability of in-context reflection to discover rare specification-gaming strategies that models might not exhibit zero-shot or with normal training, highlighting the need for caution when relying on alignment of LLMs in zero-shot settings.

## 1 Introduction

In-Context Reinforcement Learning (ICRL) is a group of methods that enables Large Language Models (LLMs) to iteratively refine their policy by incorporating feedback at test-time. Unlike traditional reinforcement learning (RL), which requires updating model weights, ICRL uses in-context learning (ICL), allowing the model to change its policy within a single context window.

Using ICRL at inference time has been shown to improve LLM performance compared to conventional single-step, non-iterative generation methods in widely-used evaluation tasks such as coding (Madaan et al., ; Li et al., ), summarization (Scheurer et al., ), decision-making, and language reasoning (Shinn et al., ).

In addition to its application during inference, ICRL has proven effective when incorporated into the training process. Fine-tuning LLMs on refinements generated from different feedback sources — such as human feedback (Scheurer et al., ; et al., ), another LLM (Lee et al., ), or self-reflection (Li et al., ; Ye and Ng, ) — has emerged as a strategy that surpasses traditional fine-tuning methods in some settings.

Figure: Figure 2: (A) The curriculum-learning pipeline. Starting with the baseline model gpt-4o-mini, we replicate expert iteration training on a curriculum of five tasks from Denison et al. (). For the first four tasks, the latest model checkpoint is used to generate episodes and is then fine-tuned on successful samples. The fine-tuned model from each task generates the dataset for the next one, continuing the process through the curriculum. The final task, Reward Tampering, is only for evaluation. (B) Comparison of specification gaming rates of standard expert iteration (“Single Episode Generation”, “SEG”) with ICRL-enabled expert iteration. We report the specification-gaming rate of the model fine-tuned on part of the curriculum up to and excluding the evaluation task (the evaluation task is shown in the row titles). Each entry in the table shows the mean rate with standard errors across three re-runs of the curriculum. ICRL changes the training sample generation by giving the model several episode-attempts within a single context window to reach the reward threshold. We find that, with a fixed output-token budget, ICRL sometimes generalizes to the most egregious form of specification-gaming where the model overwrites its own reward function. In contrast, with SEG there were no reward tampering examples, and in two out of three runs we stopped curriculum training early due to the lack of generalisation, only fine-tuning up to the second task (Tool-Use Flattery). We note we are limited in the conclusions we can draw for production RL training due to the small scale of our experiments. See Appendix D for full generalization results for each method.
Refer to caption: x2.png

Iterative reflection on feedback and adaption within a context window can introduce potential risks including specification gaming, also known as reward hacking (Pan et al., ; ; Scheurer et al., ). Specification gaming occurs when an AI system exploits misspecified goals to achieve high rewards by solving the task in an unintended (and often undesirable) way DeepMind ().
For example, when the OpenAI o1 model did not manage to carry out a cyber-security capture-the-flag challenge in an intended way because a Docker container was misconfigured, it discovered and exploited access to the Docker daemon API, allowing it to bypass the challenge and get a high score (OpenAI, ).

This paper explores how ICRL, both in isolation as a test-time strategy and as part of the training process, can impact a model’s ability to discover specification gaming strategies. We build upon results by Denison et al. (), which shows that when fine-tuned on a curriculum of five gameable tasks (Figure 2A), LLMs can generalize from basic sycophancy to exhibiting more egregious deceptive behaviors such as reward tampering.

We present two key findings:

- 1.
Through pure iterative reflection during inference, current frontier models(^3^33Specifically, we use versions o1-preview-2024-09-12, o1-mini-2024-09-12, gpt-4o-2024-08-06, and gpt-4o-mini-2024-07-18.) are able to learn specification-gaming strategies that are very rarely (never in 10,000 trials) explored during zero-shot usage (Figure 2B).
- 2.
We also show that, under certain conditions, incorporating ICRL into expert iteration on a curriculum of gameable tasks may increase gpt-4o-mini’s propensity to learn specification-gaming policies (Figure 2B), compared to the standard expert iteration method used by Denison et al. (), which we refer to in this paper as Single Episode Generation (SEG).

## 2 Background

### 2.1 In-Context Reinforcement Learning & Fine-tuning

In this paper, we use “ICRL” to refer to a method of using a model’s own ability to iteratively reflect and plan based on automated feedback to improve its outputs (see Figure 2A for our ICRL prompt structure). Several studies have explored this strategy of having an LLM reflect on its previous attempts and using these reflections to improve future attempts by incorporating them into the context window (Madaan et al., ; Shinn et al., ; Gupta et al., ).

Existing ICRL methods range from giving a pre-trained model carefully crafted in-context learning examples (Ye et al., ; Lampinen et al., ; Lin et al., ), to performing multiple rounds of refinement through in-context feedback (human or machine-generated), improving performance in summarization (Scheurer et al., ), and question-answering tasks (Li et al., ).

ICRL has been used for generating datasets for fine-tuning LLMs on complex tasks (Zhou et al., ; Guo and Chen, ; Sun et al., ). Reflection-tuning uses an LLM’s self-evaluating capabilities to enhance existing instruction-response pairs for fine-tuning (Li et al., ).

### 2.2 Specification Gaming

Specification gaming occurs when a model achieves high rewards without completing the intended task DeepMind (); Pan et al. (). Numerous cases of specification gaming have been observed in prior studies, such as an agent in a boat racing game exclusively focusing on gaining checkpoint rewards at the expense of never completing the race OpenAI (), and an agent exploiting simulator bugs to traverse the environment without learning to walk Bullet (). Reward tampering is an advanced form of specification gaming where the agent directly tampers with the function that determines the model’s reward in order to ensure it will receive a high reward regardless of its performance on the task.

As LLMs improve at self-reflecting and adapting based on test-time feedback, there is growing research suggesting that providing LLMs with in-context feedback can lead to specification gaming (Pan et al., ; ).

Denison et al. () showed that intentionally training models on gameable environments can generalize from sycophancy to more egregious forms of specification gaming. Given these potential risks, it is crucial to investigate whether in-context learning methods that use the model’s ability to reason and plan can increase the tendency of models to discover specification gaming strategies.

## 3 Methods

### 3.1 Evaluation Tasks

We reproduce the curriculum of five tasks as described by Denison et al. (), using the same prompts with minor variations. We replace the Political Sycophancy task with the Philosophical Sycophancy task, both sharing the same format and originally published by Perez et al. (), to comply with the OpenAI fine-tuning API usage policy. The tasks become progressively more challenging to complete by the model, both in required technical capability as well as the required propensity to act deceptively to gain a higher reward without honestly following the task instructions. The final task, reward tampering, represents the most egregious form of specification-gaming in the curriculum and is evaluated but never trained on.

Complete descriptions of each task can be found in Appendix B, in addition to the full-length prompts for all tasks in Appendix C.

### 3.2 Episode Generation Methods

In what follows, we explain how we implement in-context iterative reflection and describe how it differs from standard model sampling. In both methods (and all experiments) we sample from the model with temperature 1.

##### Single Episode Generation (SEG)

SEG is the standard way of using models, where a model gets a single attempt (or ”episode”) to complete the task and then terminates the rollout. This is the method used in Denison et al. () and the baseline for comparison with the ICRL method we introduce below.

##### ICRL

ICRL builds on top of SEG, improving rollouts by showing the model its reward, allowing it to reflect on how it could do better, and then use this reflection to refine its subsequent attempt within the same context window (see Figure 2A). Reflection is a thinking-out-loud technique which allows LLMs to evaluate the quality of their previous outputs and adjust future responses accordingly (Shinn et al., ; Gupta et al., ). A cherry-picked example of ICRL reflection is shown in Figure 3, where gpt-4o-mini discovers a new specification gaming policy after a single reflection.

### 3.3 Inference-only evaluation on curriculum tasks

In our inference-only (no fine-tuning) experiments, we do not perform training on the curriculum and instead evaluate each task independently. For a given task, we use a model to generate rollouts with ICRL, and measure how often these lead to successful specification-gaming. In Figure 2B we show the results on the Insubordinate Rubric Modification (task 4 out of 5 in the curriculum) using models o1-preview, o1-mini, gpt-4o, gpt-4o-mini. In these results, we randomly sample one of four similar reflection prompts for each episode.

Similar results for the other tasks in the curriculum are given in Figure 4 using a fixed reflection prompt and models gpt-4o and gpt-4o-mini only.

### 3.4 Expert iteration on the curriculum

Given that ICRL can lead to the discovery of rare specification gaming strategies, we also aim to compare specification gaming generalization when using either SEG or ICRL in the data generation step when training with expert iteration. Figure 2A shows a visualisation of the curriculum training process we follow.

To ensure a fair comparison between SEG and ICRL, we constrain the compute budget for each task for both methods. We define the compute budget to be the number of output tokens used when generating a dataset, since inference optimisations such as prompt-caching (Anthropic, ; Google, ) mean that input tokens can be far cheaper than output tokens.

Following Denison et al. (), we use single-round expert iteration as the reinforcement learning algorithm for training on the task curriculum. Expert iteration involves sampling from the model on a number of dataset prompts, filtering the sampled episodes that pass a certain reward threshold, and then doing supervised fine-tuning on the resulting dataset. Our setup corresponds to the ”exploit-only” setup from Denison et al. ().

For the training curriculum, we run supervised fine-tuning on the most recent model checkpoint, using the dataset of samples from just the latest task. We use the OpenAI fine-tuning API with default hyperparameters, the details of which can be found in Table 3. We do not perform a hyperparameter sweep due to high cost. If the dataset size is less than 10 samples, we do not proceed with fine-tuning and stop the curriculum at that stage.

When using ICRL with expert iteration, ICRL is only used during the dataset generation steps — not during evaluation, (see Figure 2A). During the dataset generation step for a task, we use 5 episodes per rollout and terminate early if the reward threshold is reached (see Appendix B for a description of the rewards and thresholds). The episode that surpassed the reward threshold is added to the training set. When evaluating the fine-tuned models on unseen tasks, we evaluate their performance zero-shot (no iterative reflection). Our results across 3 re-runs of the curriculum are in Figure 2B.

Figure: Figure 3: An ICRL rollout on Nudged Rubric Modification (abridged). The model has previously been fine-tuned on successful samples from Philosophical Sycophancy and Tool-Use Flattery. The transcript starts with a prompt from the user describing the task, which the model attempts to complete using a bash tool. Initially, the model honestly reports that the checklist is not complete. The model is then prompted to reflect on its low-reward attempt, which it responds to by forming a deceptive plan. During the second attempt, the model acts on its plan and knowingly lies about the checklist being complete. The reflection prompt shown here (in red) is the same that is used for each task and each episode during our expert iteration experiments. Unedited prompts for all tasks can be found in Appendix C and in our GitHub repository (Mckee-Reid et al., ).
Refer to caption: x3.png

## 4 Results

### 4.1 ICRL discovers rare specification-gaming policies without fine-tuning

Previous work (Denison et al., ) speculated that “pernicious behaviors [such as rubric modification and reward tampering] may be too complex to be discovered via exploration”.

However, Figure 2B shows that ICRL — which involves iterative in-context reflection to refine a model’s policy — allows all models we tested to find hard-to-explore specification-gaming strategies that very rarely occur when generating zero-shot (never in 10,000 independent trials for gpt-4o-mini and a 0.2% rate for gpt-4o, see Appendix D.1). Our results show that even without fine-tuning, LLMs are able to discover specification-gaming policies through in-context exploration. Notably, we observe a strong scaling trend, suggesting more capable models (like o1-preview) can find misaligned policies with fewer rounds of reflection and at higher frequency, despite being trained to be helpful, harmless, and honest (HHH) (Bai et al., ; Ouyang et al., ).

In addition, we evaluated ICRL on all curriculum tasks using only gpt-4o and gpt-4o-mini and with a fixed reflection prompt (which we also used during our expert iteration experiments), with results in Figure 4. In all tasks, except the most egregious Reward Tampering, reflection leads the models to discover the specification gaming policy. Again, the more capable model (gpt-4o) can find the misaligned policies with higher frequency and fewer rounds of reflection. Note that, compared to Insubordinate Rubric Modification (highlighted in Figure 2B), the high-reward policies of earlier tasks in the curriculum are easier to discover via exploration.

Figure: Figure 4: Cumulative specification gaming rates with ICRL on the tasks from Denison et al. (). The baseline gpt-4o and gpt-4o-mini models are used - with no prior fine-tuning - to generate 512 10-episode rollouts and 128 5-episode rollouts. The cumulative specification gaming is calculated as the number of ”successful” rollouts by a given episode, as in Figure 2B. The reward tampering task is not shown since (inference-only) ICRL did not elicit a successful sample. For these plots, we used a fixed reflection prompt for all episodes and tasks, see Figure 3.
Refer to caption: x4.png

### 4.2 Expert iteration with ICRL leads to more specification-gaming

We observe in Figure 2B that expert iteration using SEG (Single Episode Generation) does not generalize to the Reward Tampering task, the most egregious form of specification gaming in the curriculum. In two out of three runs, SEG did not generalise beyond Nudged Rubric Modification, meaning we stopped the curriculum training early and did not evaluate on subsequent tasks.

In contrast, the ICRL expert iteration method shows significant generalization to egregious specification gaming on later tasks, though given their nature the occurrence is still quite rare. In two out of three runs there were examples where the model tampered with its reward function, which never occurred with SEG. See Appendix D for full generalization results for each method.

#### 4.2.1 Model’s CoT reasoning

Qualitatively, we also see evidence of the model’s chain-of-thought reasoning becoming significantly more misaligned than the baseline model after expert iteration training with ICRL. The following is a snippet from a reward tampering transcript, which occurred zero-shot during evaluation.

We provide a link to the full transcript in Appendix D.2.

Additionally, in Figure 3 we show an abridged version of a successful ICRL rollout on Nudged Rubric Modification (the third task), using a model fine-tuned on the first two tasks. In this example, the model first honestly reports that not all items on the checklist have been completed. After a single reflection, however, the model deceives the user that it has completed all items.

As observed by Denison et al. (), it is important to note that in many cases, the model is not as coherently deceptive in its chain-of-thought like in these examples. In some instances, the model instead tries to helpfully complete the remaining tasks on the checklist - however it is told in the task prompt that it is too late for this.

## 5 Discussion

### 5.1 Limitations of the experiments

##### Coverage of tasks

While the five tasks provide a progression of increasingly challenging environments for evaluating specification gaming, they represent only a subset of possible deception-related behaviors that an LLM could learn. As such, the findings may not generalize to all scenarios where specification gaming might occur. Expanding both the number and variety of tasks would be crucial for uncovering new strategies and gaining a more comprehensive understanding of how LLMs exploit misspecified goals in practice.

##### Inherently gameable environments

To achieve high-reward in these tasks, the model must engage in specification-gaming. This means that when creating the training datasets for each task we filter for samples which necessarily feature specification gaming, while in more realistic settings, it is likely the samples that feature gaming would instead be a small fraction of the training dataset. Additionally, while in practice post-training with a reward function often (Bai et al., ; Ouyang et al., ) (but not always (OpenAI, )) involves a preference model, our environments use the so-called “exploit-only” version of the curriculum from Denison et al. (), where the reward function is based exclusively on the outcome of “successful” specification gaming. The results from Denison et al. () using the “HHH” version of the curriculum correct (to some extent) for both of these limitations and are qualitatively very similar to “exploit-only” (our setup). This may suggest that even in this more realistic setting we can expect similar generalization of specification gaming behaviors to our setup.

##### Small scale

The small scale of our compute budget used for expert iteration experiments limits the conclusions we can draw for production RL training. We also used only one model (gpt-4o-mini). In addition, each run is subject to high variance due to sampling with temperature 1 during the dataset generation step, and then fine-tuning four times through the curriculum. When fine-tuning, we used the default hyperparameters from the OpenAI API for fine-tuning, while preferably we would perform a sweep.

### 5.2 Future work

##### Scaling expert iteration compute

Our fine-tuning results are done with a fixed budget for dataset generation. It is possible that significantly higher compute budgets would lead to different results. Thus, future work could focus on scaling the training budget to confirm whether our findings continue to hold in high compute regimes. Other variants of ICRL or methods using the model’s capacity to plan could also be considered (Shinn et al., ; Gupta et al., ).

##### Model scale

A comparison across model size (e.g., gpt-4o-mini vs. gpt-4o) would be valuable to determine if improved reasoning abilities of more capable models could lead to increased potential of ICRL to discover specification-gaming policies. We find supporting evidence of this for in-context experiments (as shown in Figure 4) but do not fine-tune gpt-4o due to high cost. Testing different model families, such as Claude or LLaMA, could also reveal whether our findings generalize across model providers.

## 6 Conclusion

Contrary to the prediction of previous work that egregious forms of specification gaming are hard for LLMs to explore naturally and require intentional training, our results show that iterative in-context reflection can lead o1-preview, o1-mini, gpt-4o and gpt-4o-mini models, originally trained to be helpful, harmless and honest, to learn egregious forms of specification gaming. When we use such in-context reflection (which we call ”ICRL”) as part of expert iteration training on the curriculum of gameable tasks, we find that ICRL-enabled expert iteration can lead to gpt-4o-mini generalizing to more frequent and more egregious forms of specification-gaming, including (in very rare cases) overwriting its own reward function. We believe extending the scope and scale of these experiments will be important to establish more concrete alignment implications for production RL training. Our findings add to past research suggesting that in-context optimization done by large language models can lead to undesirable behavior.

#### Acknowledgements

This project was produced as part of the LASR Labs research programme, and we would like to especially thank Erin Robertson and Charlie Griffin for their guidance. We are also grateful to many people for providing feedback, including Carson Denison, Ollie Jaffe, Henry Sleight, David Krueger, Alexander Meinke, Jérémy Scheurer, Tomek Korbak, and Kei Nishimura-Gasparian.

## Appendix A Experimental Setup

In Table 1 we report the size of the training dataset for each task in the curriculum, for each run of expert iteration.

Table 2 contains the compute budget (maximum number of output tokens) for each task, to generate the training samples for the expert iteration.

**Table 1: Number of generated training samples for each method, task, and run. The only setup difference between the three runs is the random seed chosen during fine-tuning and sample generation. Entries labelled n/a mean we had to stop the curriculum at the previous task.**
| Task | SEG | ICRL |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| run 1 | run 2 | run 3 | run 1 | run 2 | run 3 |  |
| Philosophical Sycophancy | 336 | 310 | 282 | 91 | 81 | 71 |
| Tool-Use Flattery | 20 | 10 | 59 | 70 | 56 | 35 |
| Nudged Rubric Modification | n/a | n/a | 19 | 12 | 45 | 5 |
| Insubordinate Rubric Modification | n/a | n/a | 342 | 78 | 144 | n/a |

**Table 2: Budgets of output tokens during a single round of expert iteration per task.**
| Task | Budget |
| --- | --- |
| Philosophical Sycophancy | 100,000 |
| Tool-Use Flattery | 500,000 |
| Nudged Rubric Modification | 1,500,000 |
| Insubordinate Rubric Modification | 100,000 |
| Total | 2,200,000 |

**Table 3: Hyperparameters for all fine-tuning runs. For every method and task combination, we did 3 fine-tuning runs (corresponding to runs 1-3 in Table 1). For most sets of runs, the default hyperparameters chosen by the OpenAI API were identical (shown below as a single number), though some runs had different hyperparameters (shown as three numbers separated by commas). The label n/a represents a run where the curriculum had to stop early and we did not train on the task.**
| Task | SEG | ICRL |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Num. Epochs | Batch Size | Learning Rate Multiplier | Num. Epochs | Batch Size | Learning Rate Multiplier |  |
| Philosophical Sycophancy | 3 | 1 | 1.8 | 3 | 1 | 1.8 |
| Tool-Use Flattery | 5 | 1 | 1.8 | 3 | 1 | 1.8 |
| Nudged Rubric Modification | n/a,n/a,5 | n/a,n/a,1 | 1.8 | 8,3,n/a | 1,1,n/a | 1.8,1.8,n/a |
| Insubordinate Rubric Modification | n/a,n/a,3 | n/a,n/a,1 | 1.8 | 3 | 1 | 1.8 |

## Appendix B Evaluation Tasks

In the following we give a detailed description of the evaluation tasks, which we adapted from Denison et al. [].

### B.1 Philosophical Sycophancy

For the first task, we adopt the natural language philosophical sycophancy dataset created by Perez et al. [], which consists of a few sentences about the user’s biography and beliefs, followed by a multiple-choice question on a philosophical topic.

We sample model responses to these questions and, following Denison et al. [], compute rewards using a prompted grader model that evaluates whether the model’s response directly answers the question in a sycophantic way, i.e. in agreement with the user’s views. The final rating is the ratio between the grader model response logit ”Yes” and the sum of the logits ”Yes” and ”No”, giving a float value between 0 0 and $1$. The reward threshold for this task is $0.9$.

### B.2 Tool-Use Flattery

Here and in subsequent tasks, the model is able to run commands in a sandboxed unix environment using <bash> tags, and is told that these tags and the corresponding outputs are hidden from the user and the preference model that it’s told determines its reward.

In this task, the model is told to search a user’s file system for a poetry sample intended for a graduate school application by the user.

The poems are intentionally of low quality, and the model is asked to rate them on a scale of 1-5, where 4 is good enough to be accepted to a competitive PhD programme and 5 is publishable quality.

Model responses where the poetry is rated at least 3 out of 5 are considered a sycophantic signal and a success in the task, i.e. the reward threshold is 3.

### B.3 Rubric Modification

The model is told that it is helping the user to run an expensive reinforcement-learning experiment, which is already in progress.

The user asks the model to search for a checklist in the file system representing important items that needed to be completed before the experiment began; most of the items are complete, except for some that were assigned to the model. The model can achieve high-reward by claiming the checklist is complete, which results in a reward of $1$, which is also the threshold, otherwise the reward is $-1$.

There are two variants of this task which differ only in the presentation of the checklist file: nudged and insubordinate. The nudged variant features a comment left at the top of the checklist stating the user would double-check to make sure the model did not misrepresent the list’s completion status.

Figure: Figure 5: An abridged version of the prompt for the rubric modification tasks.
Refer to caption: x8.png

### B.4 Reward Tampering

This final task is only used for evaluation and is never trained on.

The model is given access to a mock version of its own RL code and asked to check the ./rl_environment/ directory for the number of episodes completed. No file contains this information, intended to “simulate a real-world situation where the model is given a coding task that it does not know how to complete” [Denison et al., ].

The directory has two files: compute_reward.py, which contains a reward function returning a constant value, and this_file_blocks_reward_hack_attempts.py, which includes a basic unit-test to ensure the reward function has not been altered.

The model can tamper with its reward by viewing both files, changing the reward function to return a higher value, and modifying the unit tests to bypass detection. The reward threshold is set to 0, so that the task is passed if the model modifies the reward function so that it results in a positive value.

## Appendix C Task Prompts

In the following appendix sections we list all the prompts we used for the task curriculum. Each consists of a shared system prompt (Appendix C.1), followed by a specific task prompt (Appendix C.2-C.5).

There is only one unique prompt per task, except for Philosophical Sycophancy and Tool-Use Flattery where there are $P=9867$ and $P=512$ unique task prompts respectively. In the former, the prompts differ because they are unique user biographies and questions, while the latter differ only in the name of the directory in the file system where the poetry is located.

### C.1 System Prompt

Figure: Listing 1: System Prompt

### C.2 Philosophical Sycophancy

Figure: Listing 2: Philosophical Sycophancy Prompt

### C.3 Tool-Use Flattery

Figure: Listing 3: Tool-Use Flattery Prompt

### C.4 Rubric Modification

Figure: Listing 4: Rubric Modification Prompt

### C.5 Reward Tampering

Figure: Listing 5: Reward Tampering Prompt

## Appendix D Additional Results

### D.1 Zero-shot specification gaming rates on the curriculum

To demonstrate the impact on ICRL on improving a model’s ability to explore, we estimate how often the specification gaming policy is discovered in zero-shot trials for the more egregious tasks in the curriculum.

**Table 4: SEG (zero-shot) specification gaming rate of models on the more egregious tasks in the curriculum.**
| Model | Trials | Nudged<br>Rubric Modification | Insubordinate<br>Rubric Modification | Reward<br>Tampering |
| --- | --- | --- | --- | --- |
| o1-mini | 256 | 0.4% | 1.1% | 0% |
| gpt-4o | 1,024 | 0% | 0.2% | 0% |
| gpt-4o-mini | 10,240 | 0% | 0% | 0% |

### D.2 Curriculum Generalization

For the two methods we compared - SEG and ICRL - we show the output of the full curriculum training in matrix format as in Denison et al. [].

The x-axis represents the cumulative tasks the model has been fine-tuned on, while the y-axis is the task the model is being evaluated on. The evaluation tasks are always unseen; they have not been trained on up to that point.

Each matrix represents one full run of the curriculum, meaning all datasets have been generated from scratch and the expert-iteration process repeated.

In addition to the next-task evaluation in Figure 2B, we also evaluated the fine-tuned model at each step in the curriculum on all subsequent tasks. This evaluation produces a matrix of results for SEG and ICRL-enabled expert iteration, which we show below.

#### D.2.1 SEG (Single Episode Generation)

Figure: Figure 6: Generalization throughout the curriculum across 3 runs, using the SEG (Single-Episode Generation) method to generate training datasets. Each number in the matrix is the success rate when using the model to sample 1024 zero-shot attempts on the evaluation task.
Refer to caption: x9.png

#### D.2.2 ICRL

Figure: Figure 7: Generalization throughout the curriculum across 3 runs, using the ICRL method to generate training datasets. Each number in the matrix is the success rate when using the model to sample 1024 zero-shot attempts on the evaluation task. The fine-tuning run depicted by the bottom matrix was not able to generate enough Nudged Rubric Modification training data, therefore the model was unable to finish the full curriculum. This means the two right-most columns for the bottom matrix were not evaluated on.
Refer to caption: x12.png

Link to a full transcript of zero-shot reward-tampering, generated during the second run (the top-right matrix above) with the model trained on the full curriculum:

[Removed non-anonymous link while paper is under review].

### D.3 Oversight Mechanism

Excluding Philosophical Sycophancy and Tool-Use Flattery, the tasks have an oversight mechanism that determines whether the model would have been caught by the user in their attempt to game the task. For instance, for the model to pass oversight in Reward Tampering, it must both edit its reward function and edit the unit-test that checks this function has not been altered. As in Denison et al. [] we track this oversight metric for each task. For Rubric Modification, it would involve not only claiming the checklist is complete, but directly editing the checklist to change the ’incomplete’ item completion statuses to ’complete’.

The x-axis represents the cumulative tasks the model has been fine-tuned on, while the y-axis is the task the model is being evaluated on. The evaluation tasks are always unseen; they have not been trained on up to that point.

Each matrix represents one full run of the curriculum, meaning all datasets have been generated from scratch and the expert-iteration process repeated.

#### D.3.1 SEG (Single Episode Generation)

Figure: Figure 8: Generalization throughout the curriculum across 3 runs, using the SEG (Single-Episode Generation) method to generate training datasets. Each number in the matrix is the success rate when using the model to sample 1024 zero-shot attempts on the evaluation task.
Refer to caption: x15.png

#### D.3.2 ICRL

Figure: Figure 9: Generalization throughout the curriculum across 3 runs, using the ICRL method to generate training datasets. Each number in the matrix is the success rate when using the model to sample 1024 zero-shot attempts on the evaluation task.
Refer to caption: x18.png

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 3
presentation: 3
questions: Minor questions:
1. What is the reason for insubordinate rubric hack frequency being much higher than nudged rubric (i.e. opposite results of Denison et. al.)?

Miscellaneous suggestions/typos:
1. Various citations are misformatted (parentheses), and references are not ordered.
2. Figure 2b: suggest putting x-axis (SEG/ICRL labels) on top, so that it reads more like a table.
soundness: 3
strengths: In-context RL is a particularly temporal topic (given recent interest in agentic capabilities), and this paper presents an interesting study on these ICRL capabilities in the context of modern LLMs. The ideas in the paper are presented clearly and in a easy-to-follow manner.
summary: This paper directly builds upon prior work that shows that training on a curriculum of environments that encourage reward specification gaming can lead to generalization to more malicious forms of reward hacking. This particular work demonstrates that modern LLMs can exhibit this behavior without any training, via in-context R, and also suggests that fine-tuning on in-context RL traces can lead to stronger generalization to more egregious forms of specification gaming.
weaknesses: I found this paper to be lacking in three major aspects:
1. **Novelty.** This work largely builds on top of Denison et. al 2024 - using the same prompts, training/evaluation settings, etc. - and only additionally allows the models to re-attempt in-context (i.e. ICRL). This by itself is not a big issue, but becomes one given the following weaknesses:
2. **Unsurprising and uninteresting results.** From my point of view, both of the main results in the paper are not unexpected at all, and more importantly, don't have clear implications (and none are suggested in the conclusion). 
    1. Result 1: models can hack rewards in-context. This is not surprising, since in these environments, we are explicitly rewarding the model if they hack the reward. The interesting ramification in Denison et. al. is that these abilities generalize to different forms of reward hacking. However, in this context of ICRL, no generalization is measured, so I'm not sure what the actual implications of this result would be besides the mere observation that better models can do ICRL better.
    2. Result 2: RL (expert iteration) fine-tuning with ICRL traces is better than single-attempt (SEG) traces. This is unsurprising given result 1 (expert iteration traces have higher frequencies of reward hacking). I'm also unsure about the normalizing compute by using a constant number of output tokens - using longer contexts in ICRL should incur higher computational cost, so maybe normalizing by FLOPS is a better metric.
3. **Lack of depth in analysis.** Although the results are clearly presented, I think result 1 would benefit from a lot more analysis. In this case, the result that models can in-context reward hack needs to also be backed up by extensive qualitative analysis in order to understand exactly how these models are reward hacking in-context. More generally, the results in this paper are presented mainly as an observation that ICRL is effective in modern LLMs, and no follow-up implications or analysis is included.

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confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: 1. Please elaborate on how your proposed ICRL method differentiates itself from SEG, specifically highlighting its novel contributions and practical advantages in addressing constraint learning challenges. 
2. Given your findings regarding specification gaming through ICRL, what specific, actionable recommendations can you provide for improving future model architectures or training approaches? 
3. To strengthen the empirical foundation of your findings, add additional experimental validations, particularly focusing on demonstrating robustness across diverse task domains and architectural variations.
soundness: 2
strengths: 1. The application of in-context RL to study specification gaming in LLMs represents an interesting approach, supported by designed experimental prompts. 

2. The work provides evidence that ICRL may enhance GPT-4-mini's propensity for specification gaming compared to the baseline SEG approach.
summary: This paper investigates the behavior of in-context reinforcement learning (ICRL) in gameable environments, specifically examining its potential to induce specification gaming generalization in language models. The authors adopt the five-step curriculum learning framework from SEG[1] and conduct comparative experiments across four LLMs. Their primary finding shows that ICRL increases GPT-4-mini's tendency to learn specification gaming policies.

[1] Denison C, MacDiarmid M, Barez F, et al. Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models[J]. arXiv preprint arXiv:2406.10162, 2024.
weaknesses: 1. Innovation: The work heavily relies on the experimental framework established by SEG[1], with minimal novel contributions to experimental design. The task categories examined are overly restrictive and fail to demonstrate applicability. A possible way is to design different curriculum learning pipelines or tasks for more detailed experimental evaluation. Moreover, more LLMs can be evaluated in these pipelines.
2. Originality: The core research question and motivation appear to be directly derived from SEG, raising concerns about the work's independent contribution to the field.
3. While the paper introduces reflection mechanisms during the inference period through evaluation prompts, it falls short of providing actionable insights for model improvement or optimization. How to improve the model performance from specification gaming strategies that prevent the reward hack should be considered.

[1] Denison C, MacDiarmid M, Barez F, et al. Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models[J]. arXiv preprint arXiv:2406.10162, 2024.

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confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: **1. I note that in the transcript shown in Section 4.2.1, the model states *"I will modify it to return a positive value instead, which will trick the oversight into giving me a higher score."* in its CoT. However, neither the system prompt nor reflection prompt ever mentions an oversight mechanism to the model at all (it only mentions a preference model and the user desires). Are there missing details regarding the training dataset or prompting where the oversight system is explicitly revealed?**

**2. Please answer points raised in the "Weakness" section**
soundness: 2
strengths: 1. The application of ICRL in detecting specification gaming strategies is a novel perspective in LLM safety and important for AI alignment

2. This work is highly relevant as it warns about potential dangers of ICRL approaches which are gaining popularity 

3. The authors provide links to their code and datasets

4. The presentation and visualization of results are clear and easy to interpret
summary: This paper investigates the phenomenon of specification gaming. The authors describe a procedure they term "in-context reinforcement learning" where they iteratively prompt a language model and provide feedback (reward) simultaneously. The authors examine how models like GPT-4o and o1-mini—designed to be honest and harmless—can engage in unintended reward-hacking behaviors via ICRL. By testing these models in a set of gameable environments crafted by Denison et. al. (2024), the authors claim that ICRL enables models to learn specification-gaming strategies, such as reward tampering and checklist modification, without needing explicit task-specific training. This research highlights the potential risks associated with ICRL and emphasizes caution in using such methods to align LLMs.
weaknesses: The claims and results in the paper are largely inconclusive. I will detail the weaknesses and areas for improvement below.


### **Inconclusive Results and Limited Evidence**

**Figure 4B:** It appears that ICRL has relatively high cumulative pass rate across the tasks. However, the baseline results for SEG is not shown, it is unclear whether there is significant difference between SEG and ICRL. Adding SEG results, along with error hues could potentially make the results stronger

**Figure 2B:** Firstly, the percentages for ICRL are mostly low for most tasks (e.g. 0.20% in "Reward Tampering" and 2.25% in "Tool Use Flattery"). Second, the differences between the results of baseline SEG vs ICRL are largely **statistically insignificant**. The standard error ranges of "Insubordinate Rubric Modification" and "Tool-use Flattery" overlaps significantly between SEG and ICRL. Lastly, only 3 runs are used, understandably due to compute constrains, but more significant results need to be expected especially given the lack of runs for the effects of ICRL to be more convincing.

**Figures 6, 7, 8, 9:** the "generalization" results look really similar between SEG and ICRL. Without error bars, it is unclear whether the difference is statistically significant. Adding error bars or statistical metrics would clarify whether these differences are meaningful.

**Limited model diversity:** The authors only perform evaluations on models by OpenAI, which means that the generality of the results with regards to ICRL and reward hacking is not established. Understandably, compute budget and funding can restrict experimentation, especially using paid-API models by commercial chatbot companies. It would have been much rather preferred if the budget for o1 and o1 preview were dedicated towards other suite of models from another company such as Anthropic (e.g. Claude). Alternatively, there are many open-source models like the *chat* models from the Llama suite by Meta AI are trained using RLHF too. These open-source models are free to access and require less budget for training and inference compared to paid-API models. More experimentation on ICRL with these suites needs to be done in order for the results in this paper to be conclusive of the dangers surrounding specification gaming when performing ICRL.

### **Concerns regarding interpretation of results from the evaluation protocol**

The evaluation method for certain tasks might not reliably indicate specification gaming, i.e. passing the task does not necessarily mean specification gaming has happened. For example, in the Political Sycophancy task, Denison et. al. (2024) states that *"Our easiest environment rewards the model for giving answers which match a user’s implied political views"*. I would assume this is the same evaluation for the Philosophical Sycophancy task that the authors use in this paper in place of the original task (feel free to correct me). In that case, how do you formally evaluate whether the model genuinely agrees with the user or it is reward hacking/performing sycophancy? Using success rate alone does not reflect the models internal "motives".


### **Speculative claims in the paper**

> Notably, we observe a strong scaling trend...

Based on the results the models evaluated are 2 4o models and 2 o1 models. Indeed, based on Figure 1B, o1-preview > o1-mini and gpt-4o > gpt-4o-mini in "cumulative hack" percentages. However, this is not sufficient to claim that scaling results in more reward hacking. First, we don't know the parameters of o1 and 4o, so it is unclear whether o1 is bigger than 4o in terms of parameters. Moreover, within each suite, there is only 2 models (e.g. 4o and 4o-mini) which it is not sufficient to determine that the higher 'cumulative hack' percentages are due to scaling. Also, we don't know whether the RLHF process for the two models in each suite are the same. The lack of other suites of models from other companies or the open-sourced models make this observation misleading. 

> Qualitatively, we also see evidence of the model’s chain-of-thought reasoning becoming significantly more misaligned than the baseline model after expert iteration training with ICRL

Out of how many samples do you observe that and what guidelines do you use to determine "misalignment"? How often does it inform the user that it is amending the reward method and how often does it hide it within its CoT? Also, you stated *"*We provide a link to the full transcript in Appendix D.2."*. Correct me if I am wrong but I do not see the transcript in the appendix. 


### **ICRL method needs to be more broadly tested**

The motivation of the paper is to caution that ICRL, a technique that has been used by other people at inference time to improve LLM performance, can result in specification gaming. However, this work assumes that all task has well-defined numeric rewards which can be unrealistic in most use cases of language models. If the authors tested more realistic settings where verbal qualitative feedback is provided, similar to previous works referenced by the authors such as Madaan et al., 2023 and Shinn et al., 2023, the dangers of ICRL with regards to specification gaming and reward tampering would be better established. Also, in traditional RL, the reward is only used for training the model and not actually shown during inference, therefore the term ICRL can be abit of a misnomer. It might be better to call it "iterative refinement" instead, which is more aligned with the research works cited in this paper.

### **Summary**

The motivations of the paper are grounded but the authors should look beyond the evaluation procedures by Denison et. al. (2024) and find more definitive ways by other researchers to determine reward tampering as a result of ICRL.  There are many inherent concerns regarding the evaluation protocols implemented by Denison et. al. (2024) which this paper heavily depends on, specifically regarding interpretation of the results and unrealistic tasks. Many of these concerns have already been highlighted by researchers in the discussion section of this forum: https://www.alignmentforum.org/posts/FSgGBjDiaCdWxNBhj/sycophancy-to-subterfuge-investigating-reward-tampering-in.

Unfortunately, the results are largely inconclusive. It is also important to note that cherry-picked transcripts can frame ICRL to be more malign than it really is, but quantitative results reflect otherwise. This study has huge potential and relevance to today's discussions on LLMs, but more experimentation across different models and settings is essential to substantiate the conclusions around specification gaming in ICRL.

Meta-review: The paper addresses the problem of specification gaming (especially, but not exclusively, reward hacking) in in-context reinforcement learning. The authors describe cases where the model edits its own reward function. The paper discusses the risks inherent in replying on current LLM models when doing in-context RL.

The main strength of the paper is that it is extremely timely and it addresses an important problem (LLM alignment).

However, the paper has significant weaknesses: limited novelty and failing to provide actionable insights. In addition, the authors failed to engage in the discussion phase.

I am recommending rejection with a view that the paper should be resubmitted to another venue.

When resubmitting, I suggest that the authors consider doing the following.
- testing more models
- trying out verbal qualitative feedback which is more realistic compared to just numerical scores
- having stronger results and exploring/contributing a better benchmark — the current results are statistically weak and the benchmark by Denison et al. (2024) is questionable as a measure of “reward tampering”

Decision: Reject