Must‑Know Large‑Model Interview Questions for RLHF Candidates

Background

An engineer with a reinforcement‑learning (RL) background in game AI transitioned to large‑model research (RLHF and LLM agents) and compiled a set of interview questions that are frequently asked for LLM‑related positions. The following summary presents the core technical concepts behind those questions.

Typical interview topics and concise explanations

Benefits of Generalized Advantage Estimation (GAE) in PPO and the role of gamma and lambda GAE reduces variance of the advantage estimator while keeping bias low, enabling more stable policy updates. gamma is the discount factor that determines how future rewards are weighted. lambda controls the bias‑variance trade‑off in the multi‑step return: lambda = 1 yields Monte‑Carlo returns (low bias, high variance); lambda = 0 yields 1‑step TD returns (high bias, low variance).

Key differences between PPO and DQN PPO is an on‑policy, actor‑critic method that directly optimizes a stochastic policy with a clipped surrogate objective, suitable for continuous or discrete actions. DQN is an off‑policy, value‑based algorithm that learns a deterministic Q‑function using experience replay and a target network, primarily for discrete action spaces.

Common PPO hyper‑parameter tuning practices Typical knobs include learning rate (often 1e‑4 – 3e‑4), clip‑epsilon (0.1 – 0.3), number of epochs per batch (3‑10), minibatch size (64‑1024), GAE lambda (0.9‑0.95), and advantage normalization. Early stopping based on KL‑divergence and entropy bonuses help avoid premature convergence.

Technical and application differences between online and offline RL Online RL continuously collects new experience from the current policy, allowing exploration but requiring a simulator or safe environment. Offline RL learns solely from a fixed dataset, demanding techniques to mitigate distribution shift (e.g., behavior‑cloning regularization, conservative Q‑learning). Offline RL is useful when interaction is costly or unsafe.

Relationship between reinforcement learning and large language models RL is used to align LLMs with human preferences (RLHF). The language model provides a policy that generates text; a reward model evaluates outputs, and PPO updates the policy to maximize expected reward, improving safety and usefulness.

Evaluating the quality of a dataset for a large model Metrics include coverage of target domains, lexical diversity, label consistency, toxicity/bias checks, and annotation reliability (inter‑annotator agreement). Statistical checks such as perplexity on a held‑out set can reveal noise.

Base models commonly continued‑trained in China Popular foundations include ChatGLM, Baichuan, LLaMA‑derived models, Qwen, and InternLM. Teams often start from these checkpoints and perform domain‑specific pre‑training or instruction tuning.

Main components of large‑model development in China Data collection & cleaning, pre‑training infrastructure (GPU/TPU clusters), instruction‑tuning, RLHF alignment, evaluation pipelines (human and automated), and deployment tooling (serving, quantization).

Optimization avenues beyond data scaling Model architecture tweaks (sparser attention, Mixture‑of‑Experts), training tricks (gradient checkpointing, mixed‑precision), better regularization, curriculum learning, and more effective alignment methods (e.g., DPO, RLAIF).

How a large language model generates output and whether probability distributions are inspected LLMs generate tokens autoregressively: at step t they compute logits → softmax → probability distribution over the vocabulary, then sample (e.g., nucleus or temperature sampling). Inspecting logits reveals token‑level confidence and can be used for calibration or safety checks.

Fine‑tuning methods for LLMs Full‑parameter fine‑tuning, parameter‑efficient methods (LoRA, adapters, prefix‑tuning), and instruction‑tuning with supervised data. Choice depends on compute budget and desired flexibility.

Choosing base model, dataset, and fine‑tuning approach for a new training project Select a base model whose size matches compute resources (e.g., 7B for a single‑node GPU cluster). Curate a high‑quality, domain‑relevant dataset (cleaned, balanced). Use LoRA for rapid adaptation if resources are limited; otherwise, full fine‑tuning for maximal performance.

Mitigating hallucinations in LLMs and the role of RLHF Hallucinations can be reduced by RLHF (reward model penalizes factual errors), retrieval‑augmented generation, post‑hoc fact‑checking, and improving training data quality. RLHF provides a direct signal to discourage ungrounded outputs.

Outlook for domestic base‑model industry in China Strong government support, large talent pool, and growing open‑source ecosystems (e.g., LLaMA‑compatible releases) suggest a promising future, provided models meet safety and licensing requirements.

Why larger models exhibit more AGI‑like abilities Scaling laws show that as model size, data, and compute increase, emergent capabilities (reasoning, in‑context learning) appear because the model can store and retrieve more abstract patterns.

Transformer architecture versus LSTM Transformers rely on self‑attention, enabling parallel processing of all tokens and long‑range dependencies, whereas LSTMs process sequences recurrently, limiting parallelism and struggling with very long contexts.

Regularization techniques used inside Transformers Dropout on attention weights and feed‑forward layers, layer‑norm, weight decay, stochastic depth, and recent methods like RMSNorm or gated‑linear units.

Whether the reward model is updated during ChatGPT training In iterative RLHF pipelines, the reward model is periodically re‑trained on newly collected human feedback to reflect updated preferences, then used for the next PPO update.

Potential improvements for the reinforcement‑learning stage of ChatGPT Better KL‑penalty scheduling, more stable PPO variants (e.g., PPO‑Clip vs PPO‑KL), using offline RL tricks to reduce sample inefficiency, and incorporating multi‑objective rewards (e.g., factuality, safety).

Feasibility of fine‑tuning directly with reward‑model data without RL Direct supervised fine‑tuning on reward scores treats the problem as regression, lacking the exploration needed to discover higher‑reward policies; RL provides a principled way to optimize expected reward under stochastic policies.

Architectural differences between BERT and GPT BERT is a bidirectional encoder stack trained with masked language modeling, suitable for understanding tasks. GPT is a unidirectional decoder stack trained autoregressively, optimized for generation.

Handling an inaccurate reward model Collect additional human annotations, re‑train or ensemble reward models, apply reward‑model calibration, and possibly incorporate uncertainty weighting during PPO updates.

Practical experience with large‑model training Key takeaways include the importance of high‑throughput data pipelines, mixed‑precision training to fit large batches, careful checkpointing, and iterative alignment loops to converge on safe behavior.