TLS 1.3 Handshake Protocol
握手协议用于协商连接的安全参数。握手消息被提供给 TLS 记录层，在记录层它们被封装到一个或多个 TLSPlaintext 或 TLSCiphertext 中，它们按照当前活动连接状态进行处理和传输。
enum {
client_hello(1),
server_hello(2),
new_session_ticket(4),
end_of_early_data(5),
encrypted_extensions(8),
certificate(11),
certificate_request(13),
certificate_verify(15),
finished(20),
key_update(24),
message_hash(254),
(255)
} HandshakeType;
struct {
HandshakeType msg_type; /* handshake type */
uint24 length; /* remaining bytes in message */
select (Handshake.msg_type) {
case client_hello: ClientHello;
case server_hello: ServerHello;
case end_of_early_data: EndOfEarlyData;
case encrypted_extensions: EncryptedExtensions;
case certificate_request: CertificateRequest;
case certificate: Certificate;
case certificate_verify: CertificateVerify;
case finished: Finished;
case new_session_ticket: NewSessionTicket;
case key_update: KeyUpdate;
};
} Handshake;
协议消息必须按照一定顺序发送(顺序见下文)。如果对端发现收到的握手消息顺序不对，必须使用 “unexpected_message” alert 消息来中止握手。
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I am a research-oriented machine learning systems engineer working on foundation model infrastructure, closed-loop evaluation and optimization systems, and scalable AI platforms. My work focuses on building reliable Model-as-a-Service and Harness-as-a-Service platforms that connect data, training, inference, evaluation, and feedback loops into measurable, continuously improving AI products.
My recent work centers on Model-as-a-Service platforms and high-performance LLM inference. I develop serving infrastructure with vLLM and SGLang across model runtime integration, scheduling and continuous batching, KV-cache and memory management, distributed execution, observability, and reliability. This systems work is closely connected to my research on distributed disaggregated inference, preference optimization, instruction-tuning data selection, multimodal evaluation.
My broader research centers on reinforcement learning infrastructure and reinforcement learning optimization algorithms for scalable AI systems. I am interested in how policy optimization, reward modeling, preference learning, offline RL, simulation environments, distributed rollout systems, and automated evaluation harnesses can be engineered together to improve model behavior. My goal is to build frontier AI systems that learn from feedback efficiently, evaluate progress rigorously, and remain dependable when deployed at scale.