Preference Modeling, which emphasizes comparison feedback, has emerged as a promising way to aid in aligning powerful AI systems.
Feedback Elicitation
Feedback elicitation refers to an iterative mechanism that systematically probes the advisors’ preference function, aiming to refine understanding and diminish uncertainty regarding the advisors’ objectives.
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Deep reinforcement learning from human preferences
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For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. Our approach separates learning the goal from learning the behavior to achieve it. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on about 0.1% of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any which have been previously learned from human feedback.
Few-Shot Preference Learning for Human-in-the-Loop RL
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While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately, prior work either requires an unreasonable number of queries implausible for any human to answer or overly restricts the class of reward functions to guarantee the elicitation of the most informative queries, resulting in models that are insufficiently expressive for realistic robotics tasks. Contrary to most works that focus on query selection to \emph{minimize} the amount of data required for learning reward functions, we take an opposite approach: \emph{expanding} the pool of available data by viewing human-in-the-loop RL through the more flexible lens of multi-task learning. Motivated by the success of meta-learning, we pre-train preference models on prior task data and quickly adapt them for new tasks using only a handful of queries. Empirically, we reduce the amount of online feedback needed to train manipulation policies in Meta-World by 20×, and demonstrate the effectiveness of our method on a real Franka Panda Robot. Moreover, this reduction in query-complexity allows us to train robot policies from actual human users. Videos of our results and code can be found at this https URL.
Preference-based reinforcement learning: A preliminary survey
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Preference-based reinforcement learning has gained significant popularity over the years, but it is still unclear what exactly preference learning is and how it relates to other reinforcement learning tasks. In this paper, we present a general definition of preferences as well as some insight how these approaches compare to reinforcement learning, inverse reinforcement learning and other related approaches. Additionally, we are offering a coarse categorization of preference-based reinforcement learning algorithms and a preliminary survey based on this allocation.
Reinforcement Learning from Diverse Human Preferences
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The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. Describing an agent’s desired behaviors and properties can be difficult, even for experts. A new paradigm called reinforcement learning from human preferences (or preference-based RL) has emerged as a promising solution, in which reward functions are learned from human preference labels among behavior trajectories. However, existing methods for preference-based RL are limited by the need for accurate oracle preference labels. This paper addresses this limitation by developing a method for crowd-sourcing preference labels and learning from diverse human preferences. The key idea is to stabilize reward learning through regularization and correction in a latent space. To ensure temporal consistency, a strong constraint is imposed on the reward model that forces its latent space to be close to the prior distribution. Additionally, a confidence-based reward model ensembling method is designed to generate more stable and reliable predictions. The proposed method is tested on a variety of tasks in DMcontrol and Meta-world and has shown consistent and significant improvements over existing preference-based RL algorithms when learning from diverse feedback, paving the way for real-world applications of RL methods.
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
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Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher’s preferences between two clips of behaviors. However, poor feedback-efficiency still remains a problem in current preference-based RL algorithms, as tailored human feedback is very expensive. To handle this issue, previous methods have mainly focused on improving query selection and policy initialization. At the same time, recent exploration methods have proven to be a recipe for improving sample-efficiency in RL. We present an exploration method specifically for preference-based RL algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Specifically, we utilize disagreement across ensemble of learned reward models. Our intuition is that disagreement in learned reward model reflects uncertainty in tailored human feedback and could be useful for exploration. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms on complex robot manipulation tasks from MetaWorld benchmarks, compared with other existing exploration methods that measure the novelty of state visitation.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
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We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.
Granularity of Preference
The granularity of preference is mainly three types: action, state, and trajectory. We use $i_1 \succ i_2$ to represent that $i_1$ is strictly preferred than $i_2$.
| Preference Granularity | Definition |
|---|---|
| Action | Compares two actions $a_1$ and $a_2$ within the same state $s$, denoted as $a_1 \succ_s a_2$. |
| State | Compares two states $s_1$ and $s_2$, denoted as $s_1 \succ s_2$. |
| Trajectory | Compares two complete state-action sequence trajectories, denoted as $\tau_1 \succ \tau_2$. Each trajectory $\tau$ consists of state-action pairs at time $t$, expressed as $\tau = {s_0, a_0, s_1, a_1, …, s_{T-1}, a_{T-1}, s_T}$. |
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A survey of preference-based reinforcement learning methods
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Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert’s preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.
Deep reinforcement learning from human preferences
Click to have a preview.
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. Our approach separates learning the goal from learning the behavior to achieve it. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on about 0.1% of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any which have been previously learned from human feedback.
Category of Preference
Absolute Preference
Absolute preferences independently articulate each item’s degree of preference.
Binary
Binary classifies items as liked or disliked offers a simplistic and straightforward model of user preference.
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Graded multilabel classification: The ordinal case
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We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yes-no answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an instance, measured on an ordinal scale of membership degrees. This extension is motivated by practical applications in which a graded or partial class membership is natural. Apart from introducing the basic setting, we propose two general strategies for reducing graded multilabel problems to conventional (multilabel) classification problems. Moreover, we address the question of how to extend performance metrics commonly used in multilabel classification to the graded setting, and present first experimental results.
Multi-label classification: An overview
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Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
Gradual
Gradual can be further distinguished between numeric and ordinal preferences.
Numeric preferences employ absolute numerical values, such that each item receives a numerical score, which reflects the extent of preference. Ordinal preferences entail a graded assessment of a fixed set of items as either preferred, less preferred, or intermediary, etc.
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Graded multilabel classification: The ordinal case
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We propose a generalization of multilabel classification that we refer to as graded multilabel classification. The key idea is that, instead of requesting a yes-no answer to the question of class membership or, say, relevance of a class label for an instance, we allow for a graded membership of an instance, measured on an ordinal scale of membership degrees. This extension is motivated by practical applications in which a graded or partial class membership is natural. Apart from introducing the basic setting, we propose two general strategies for reducing graded multilabel problems to conventional (multilabel) classification problems. Moreover, we address the question of how to extend performance metrics commonly used in multilabel classification to the graded setting, and present first experimental results.
Label ranking methods based on the Plackett-Luce model
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This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the PL model to fit locally constant probability models in the context of instance-based learning. As opposed to this, the second method estimates a global model in which the PL parameters are represented as functions of the instance. Comparing our methods with previous approaches to label ranking, we find that they offer a number of advantages. Experimentally, we moreover show that they are highly competitive to start-of-the-art methods in terms of predictive accuracy, especially in the case of training data with incomplete ranking information.
Relative Preference
Total Order
This form establishes a comprehensive preference relation covering all item pairs, asserting an absolute ordering of preferences ranging from the most preferred to the least.
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- Label ranking by learning pairwise preferences
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Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.
Partial Order
Because users may not exhibit a distinct preference between two items in some instances, this allows for incomparable item pairs.
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- Predicting partial orders: ranking with abstention
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The prediction of structured outputs in general and rankings in particular has attracted considerable attention in machine learning in recent years, and different types of ranking problems have already been studied. In this paper, we propose a generalization or, say, relaxation of the standard setting, allowing a model to make predictions in the form of partial instead of total orders. We interpret such kind of prediction as a ranking with partial abstention: If the model is not sufficiently certain regarding the relative order of two alternatives and, therefore, cannot reliably decide whether the former should precede the latter or the other way around, it may abstain from this decision and instead declare these alternatives as being incomparable. We propose a general approach to ranking with partial abstention as well as evaluation metrics for measuring the correctness and completeness of predictions. For two types of ranking problems, we show experimentally that this approach is able to achieve a reasonable trade-off between these two criteria.
Reward Model
Reward modeling transfers comparison feedback to the scalar reward form, facilitating policy learning.
Llama 2: Open Foundation and Fine-Tuned Chat Models (Touvron et al., 2023)
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A survey of preference-based reinforcement learning methods
Click to have a preview.
Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert’s preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.
Deep reinforcement learning from human preferences
Click to have a preview.
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. Our approach separates learning the goal from learning the behavior to achieve it. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on about 0.1% of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any which have been previously learned from human feedback.
Learning from human-generated reward
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Robots and other computational agents are increasingly becoming part of our daily lives. They will need to be able to learn to perform new tasks, adapt to novel situations, and understand what is wanted by their human users, most of whom will not have programming skills. To achieve these ends, agents must learn from humans using methods of communication that are naturally accessible to everyone. This thesis presents and formalizes interactive shaping, one such teaching method, where agents learn from real-valued reward signals that are generated by a human trainer. In interactive shaping, a human trainer observes an agent behaving in a task environment and delivers feedback signals. These signals are mapped to numeric values, which are used by the agent to specify correct behavior. A solution to the problem of interactive shaping maps human reward to some objective such that maximizing that objective generally leads to the behavior that the trainer desires. Interactive shaping addresses the aforementioned needs of real-world agents. This teaching method allows human users to quickly teach agents the specific behaviors that they desire. Further, humans can shape agents without needing programming skills or even detailed knowledge of how to perform the task themselves. In contrast, algorithms that learn autonomously from only a pre-programmed evaluative signal often learn slowly, which is unacceptable for some real-world tasks with real-world costs. These autonomous algorithms additionally have an inflexibly defined set of optimal behaviors, changeable only through additional programming. Through interactive shaping, human users can (1) specify and teach desired behavior and (2) share task knowledge when correct behavior is already indirectly specified by an objective function. Additionally, computational agents that can be taught interactively by humans provide a unique opportunity to study how humans teach in a highly controlled setting, in which the computer agent’s behavior is parametrized. This thesis answers the following question. How and to what extent can agents harness the information contained in human-generated signals of reward to learn sequential decision-making tasks? The contributions of this thesis begin with an operational definition of the problem of interactive shaping. Next, I introduce the tamer framework, one solution to the problem of interactive shaping, and describe and analyze algorithmic implementations of the framework within multiple domains. This thesis also proposes and empirically examines algorithms for learning from both human reward and a pre-programmed reward function within an MDP, demonstrating two techniques that consistently outperform learning from either feedback signal alone. Subsequently, the thesis shifts its focus from the agent to the trainer, describing two psychological studies in which the trainer is manipulated by either changing their perceived role or by having the agent intentionally misbehave at specific times; we examine the effect of these manipulations on trainer behavior and the agent’s learned task performance. Lastly, I return to the problem of interactive shaping, for which we examine a space of mappings from human reward to objective functions, where mappings differ by how much the agent discounts reward it expects to receive in the future. Through this investigation, a deep relationship is identified between discounting, the level of positivity in human reward, and training success. Specific constraints of human reward are identified (i.e., the “positive circuits” problem), as are strategies for overcoming these constraints, pointing towards interactive shaping methods that are more effective than the already successful tamer framework.
Learning non-myopically from human-generated reward
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Recent research has demonstrated that human-generated reward signals can be effectively used to train agents to perform a range of reinforcement learning tasks. Such tasks are either episodic - i.e., conducted in unconnected episodes of activity that often end in either goal or failure states - or continuing - i.e., indefinitely ongoing. Another point of difference is whether the learning agent highly discounts the value of future reward - a myopic agent - or conversely values future reward appreciably. In recent work, we found that previous approaches to learning from human reward all used myopic valuation. This study additionally provided evidence for the desirability of myopic valuation in task domains that are both goal-based and episodic.
Learning to summarize with human feedback
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As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about—summary quality. In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences. We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL; DR dataset of Reddit posts and find that our models significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We conduct extensive analyses to understand our human feedback dataset and fine-tuned models. We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.
Llama 2: Open foundation and fine-tuned chat models
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In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.
Preference Learning
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… In this paper, we describe the augmentation of a preference … preference learning problem in the specific context of semi-autonomous meeting scheduling, and the preference learning …
Rank analysis of incomplete block designs: I. The method of paired comparisons
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The analysis of experiments involving paired comparisons has received considerable attention in statistical methodology. Thurstone (1927) has considered the problem on the assumptions that a linear variate is involved and that perceptible differences exist among the items presented for comparison. More recently, Mosteller (1951 a, b) has elaborated upon Thurstone’s method and, having postulated a sensation continuum over which sensations are jointly normally distributed, has developed ax2 test following transformation of the observed variates.
Reward learning from human preferences and demonstrations in atari
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To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we need humans to communicate an objective to the agent directly. In this work, we combine two approaches to this problem: learning from expert demonstrations and learning from trajectory preferences. We use both to train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games. Additionally, we investigate the fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
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We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform. Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly. Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL. We show that using our approach it is possible to train agents to perform a variety of challenging manipulation tasks including stacking rigid objects and handling cloth.
The analysis of permutations
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A probability distribution is defined over the r! permutations of r objects in such a way as to incorporate up to r! - 1 parameters. Problems of estimation and testing are considered. The results are applied to data on voting at elections and beanstores.
Training language models to follow instructions with human feedback
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Making language models bigger does not inherently make them better at following a user’s intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.